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31,458 Article Results

Classification of endometrial adenocarcinoma using histopathology images with extreme learning machine method

10.11591/ijeecs.v36.i2.pp961-971
Riries Rulaningtyas , Anny Setijo Rahaju , Rosa Amalia Dewi , Ummi Hanifah , Endah Purwanti , Osmalina Nur Rahma , Katherine Katherine
As many as 70-80% of endometrial cancer cases are endometrial adenocarcinoma. Histopathological assessment is based on the degree of differentiation, into well-differentiated, moderate-differentiated, and poorly-differentiated. Management and prognosis differ between grades, so differential diagnosis in determining the degree of tumor differentiation is crucial for appropriate treatment decisions. Histopathological image analysis offers detailed diagnostic results, but manual analysis by a pathologist is very complicated, error-prone, quite tedious, and time-consuming. Therefore, an automatic diagnostic system is needed to assist pathologists in grading the tumor. This research aims to determine the degree of differentiation of endometrial adenocarcinoma based on histopathological images. The extreme learning machine (ELM) method performs image classification with gray level run long matrix (GLRLM) features and a combination of local binary pattern (LBP)-GLRLM features as input. Experimental results show that the ELM model can achieve satisfactory performance. Training accuracy, testing accuracy, and model precision with GLRLM features were 97.13%, 91.33%, and 80% and combined LBPGLRLM features were 91.03%, 71.33%, and 100%. Overall, the model created can determine the degree of tumor differentiation and is useful in providing a second opinion for pathologists.
Volume: 36
Issue: 2
Page: 961-971
Publish at: 2024-11-01

Utilization of learning media based on augmented reality on design material network topology

10.11591/ijeecs.v36.i2.pp1083-1091
Manda Rohandi , Jemmy A. Pakaja , Arip Mulyanto , Dian Novian , Hermila A. , Sri Ayu Ashari , Bariq Nugraha
Augmented reality (AR) is a growing technology that has great potential in the field of education. This article explores using AR as an interactive learning medium in a secondary education environment. The study involves the implementation of AR in network topology material to enhance student engagement and understanding. This research consists of the design and development of AR applications following the curriculum of the network topology subject at SMK Negeri 1 South Bulango using the waterfall model. The results showed that using AR-based learning media can increase student engagement in the learning process. Three-dimensional visualization of network topology design can improve students’ interest and motivation to understand the material better. AR allows students to interact directly with the network topology design model.
Volume: 36
Issue: 2
Page: 1083-1091
Publish at: 2024-11-01

Effects of TiO2 in graphene-quantum-dot film on lighting color uniformity of a white light-emitting diodes

10.11591/ijeecs.v36.i2.pp800-807
Phan Xuan Le , Pham Hong Cong
Improvement in color uniformity of white light-emitting diodes (WLED) is one of the imperative goals for high-quality solid-state illumination. The conventional WLED model with a single yellow phosphor YAG:Ce3+ (TiO2@GD) is proposed to fulfill this goal. The TiO2@GD composites prove to possess excellent biocompatibility, low toxicity, and thermal and chemical stability, holding great potential in high-power WLED production. By maintaining a constant GDs content of 10 wt%, the research explores the impact of varying TiO2 doping concentrations on the lighting performance of the WLEDs via the mean of light scattering. The TiO2@GD layer also induces a red-shift in the emitted light spectrum, contributing to a reduction in color variation. While a decline in luminosity and color rendering performance becomes evident with excessive TiO2 content, the study underscores the potential of TiO2@GD as a viable diffusing layer for LEDs to obtain improved angular uniformity of color distribution.
Volume: 36
Issue: 2
Page: 800-807
Publish at: 2024-11-01

Comparative analysis on liver benchmark datasets and prediction using supervised learning techniques

10.11591/ijeecs.v36.i2.pp1043-1051
Tilakachuri Balakrishna , Jagadeeswara Rao Annam , Dasari Haritha
Disease diagnosis is most challenging task today. Different datasets are available in web source that contains important features to diagnose the diseases. This paper explores different classification algorithms on medical liver bench mark datasets like BUPA and Indian Liver patient dataset (ILPD). The ILPD is best fit for the model and also gives high classifier accuracy. In proposed model the following classifiers like Naïve Bayes (NB), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classification, multi-layer perceptron (MLP), artificial neural network (ANN), deep belief network (DBN) and probabilistic neural network (PNN) are used. The results shown that ILPD is best dataset for all classifiers and RF classification in particular is best classifier.
Volume: 36
Issue: 2
Page: 1043-1051
Publish at: 2024-11-01

Artificial intelligence in accounting and auditing: bibliometric analysis in Scopus 2020-2023

10.11591/ijeecs.v36.i2.pp1319-1328
Jorge Miguel Chávez-Díaz , Laura Aquiño-Perales , Jorge Luis De-Velazco-Borda , Juan Alberto Villagómez-Chinchay , Willian Sebastian Flores-Sotelo
El propósito del estudio fue presentar los resultados de un análisis bibliométrico y revisión de literatura sobre la producción científica relacionada con la inteligencia artificial aplicada a la contabilidad y auditoría, contenida en la base de datos Scopus entre 2020 y 2023. Para la primera parte, se realizó un análisis descriptivo y cuantitativo. Se utilizó análisis bibliométrico con búsqueda de palabras clave en la base de datos Scopus. Para la segunda parte se siguió un enfoque subjetivo basado en un análisis cualitativo a partir de la interpretación del autor. Ambos enfoques fueron considerados por su complementariedad. Se identificaron las principales características cuantitativas de las revistas, autores, artículos, estructura conceptual y estructura social. Asimismo, las implicaciones éticas de la Inteligencia Artificial aplicada a la contabilidad y auditoría, y la forma en que impacta en la contabilidad, la auditoría fiscal, la estrategia financiera y la toma de decisiones que contribuyen a la creación de valor para su organización. Cambie la aversión a la IA por la adaptabilidad y la comprensión de que se extrajeron sus auditorías y contabilidad forense. El trabajo del contable-auditor estará cada vez más informatizado. Deberían centrarse más en el análisis, hay que transformar el perfil profesional de forma sinérgica con la inteligencia artificial. El estudio pretende servir como base teórica para futuras investigaciones.
Volume: 36
Issue: 2
Page: 1319-1328
Publish at: 2024-11-01

Development of an IoT-based sleep pattern monitoring system for sleep disorder detection

10.11591/ijeecs.v36.i2.pp777-784
Muhammad Nur Ikhwan Md Shahrum , Ida Syafiza Md Isa , Nur Latif Azyze Mohd Shaari Azyze , Haslinah Mohd Nasir , Tole Sutikno
Inadequate sleep can cause various health problems including heart disease and obesity. In this work, a sleep monitoring system that monitors human sleep patterns is developed using the internet of things (IoT) and Raspberry Pi. The system is designed to record any detected movements and process the data using machine learning to provide valuable insight into a person’s sleep patterns including sleep duration, the time taken to fall asleep, and the frequency of waking up. This information is very useful to provide the sleep disorder diagnostics of an individual including restless leg, parasomnia and insomnia syndrome besides giving recommendations to improve their sleep quality. Also, the system allows the processed data to be stored in the cloud database which can be accessed through a mobile application or web interface. The performance of the system is evaluated in terms of its accuracy and reliability in detecting sleep order diagnostics. Based on the confusion matrix, the results show the accuracy of the system is 90.32%, 91.80%, and 91.80% in detecting the restless leg, parasomnia and insomnia syndrome, respectively. Meanwhile, the system showed high reliability in monitoring 10 participants for 8 hours and updated the recorded data and its analysis in the cloud.
Volume: 36
Issue: 2
Page: 777-784
Publish at: 2024-11-01

An active two-stage class-J power amplifier design for smart grid’s 5G wireless networks

10.11591/ijres.v13.i3.pp625-642
Nagisetty Sridhar , Chinnaiyan Senthilpari , Mardeni Roslee , Wong Hin Yong
The wireless communication networks in the smart grid’s advanced metering infrastructure (AMI) applications need 5G technology to support large data transmission efficiently. As the 5G wireless communication network’s overall bandwidth (BW) and efficiency depend on its power amplifier (PA), in this work, a two-stage class-J power amplifier’s design methodology that operates at 3.5 GHz centre frequency by utilizing the CGH40010F model gallium nitride (GaN) transistor is presented. The proposed design methodology involves proper designing of input, output, and interstage matching networks to achieve class-J operation with improved power gain over desired BW using the advanced design system (ADS) electronic design automation (EDA) tool and estimating its integration feasibility through active element-based design approach using the Mentor Graphics EDA tool. The proposed PA provides 54% drain efficiency (D.E), 53% power added efficiency (PAE) with a small signal gain of 27 dB at 3.5 GHz and 41 dBm power output with 21 dB of improved power gain across a BW of around 400 MHz using 28 V power supply into 50 Ω load. By replacing the two-stage PA's passive elements with active elements, its layout size is estimated to be (15.5×29.2) μm2 . The results of the proposed PA exhibit its integration feasibility and suitability for the smart grid’s 5G wireless networks.
Volume: 13
Issue: 3
Page: 625-642
Publish at: 2024-11-01

Internet based highly secure data transmission system in health care monitoring system

10.11591/ijres.v13.i3.pp681-686
Gubbala Bhaskar Phani Ram , Shankar Thirunarayanan
The health care systems in our contemporary countries are advancing rapidly in terms of maturity and professionalism. In an effort to alleviate the current burden on the public health system and boost the popularity of regular health self-checks, this method has been developed for producing prediagnoses that are easier to use, quicker, and more accurate. To ascertain how well the heart is circulating oxygen throughout the body, a pulse test, a painless examination that measures an individual's degree of oxygen saturation, is used. It can be used to evaluate the state of any patient with a disease, particularly those with pulmonary problems. Diseases in these patients could need ongoing observation and care. Our system comes to the rescue in order to resolve this problem. This portable system is simple to use and may be taken anywhere by the subject. The internet of things (IoT) will update the pertinent parameters. This health monitoring system's controller is made up of an adaptor, a saturation of peripheral oxygen (SPO2 ) sensor (a blood oxygen meter), a temperature sensor, a heart rate sensor, a WiFi module, and a liquid crystal display (LCD).
Volume: 13
Issue: 3
Page: 681-686
Publish at: 2024-11-01

Microstrip patch antenna for energy harvesting in smart buildings

10.11591/ijeecs.v36.i2.pp923-932
Stanley Glenn E. Brucal , Aaron Don M. Africa , Julian Carlos P. Chavez , Nathan H. Devera , Philip Martin Emmanuel A. Escamilla , John Louie L. Payuyo
The present study analyzes the microstrip antenna design for wireless power transfer in smart buildings, harnessing the ambient electromagnetic radiation due to common electronic gadgets that energize wireless sensor networks, computing devices, and connected appliances. With the increased number of these devices, so does the potential for health problems caused by electromagnetic radiation. However, these devices also provide a renewable energy source through their emissions. This study suggests the creation of a 5G Microstrip antenna that enhances the absorption of this radiation for the purpose of recharging batteries in smart buildings. The design capitalizes on the inherent low-profile and cost-effective features of microstrip antennas, making them well-suited for incorporation into building infrastructure and 5G wireless technologies. Although each individual device emits a little amount of energy, the combined effect achieved by advanced antenna design and power converters is anticipated to result in a substantial energy production. The antenna designer tool from MATLAB was used to carry out a conceptual simulation of the microstrip antenna. This has set up the framework of a feasible way of predicting the performance with high efficiency and sustainability for a wireless power transfer (WPT) system.
Volume: 36
Issue: 2
Page: 923-932
Publish at: 2024-11-01

Performance of dyslexia dataset for machine learning algorithms

10.11591/ijeecs.v36.i2.pp994-1001
J. Jincy , P. Subha Hency Jose
Learning disability is a condition usual amongst most populace due to poor phonological capability in humans making them impaired. One such neurological disorder is developmental dyslexia, a lack of reading and writing skills leading to difficulty in school education. The essential causes of developmental dyslexia are the consumption of more drug treatments during pregnancy, the over-the-counter purchase of medicines for minor ailments without the recommendation of physicians, and uncared-for head accidents during early life. The occurrence of this trouble is acute in India. Attempts were made by many to detect dyslexic children to reduce the intensity of this hassle. In this proposed effort, machine learning is used to locate significant styles characterizing people using EEG samples. A dataset is used for examination of developmental dyslexia, and classification is done using K nearest neighbor (KNN), decision tree, linear discriminant analysis (LDA), and support vector machine (SVM) to evaluate the performance. This piece of research work is done on MATLAB to provide results on simulation with classification accuracy of 90.76% for SVM, sensitivity of 89% for SVM, and LDA with 91.89% specificity for SVM providing optimum yield.
Volume: 36
Issue: 2
Page: 994-1001
Publish at: 2024-11-01

RecommendRift: a leap forward in user experience with transfer learning on netflix recommendations

10.11591/ijeecs.v36.i2.pp1218-1225
Surabhi Anuradha , Pothabathula Naga Jyothi , Surabhi Sivakumar , Martha Sheshikala
In today’s fast-paced lifestyle, streaming movies and series on platforms like  Netflix is a valued recreational activity. However, users often spend considerable time searching for the right content and receive irrelevant recommendations, particularly when facing the “cold start problem” for new users. This challenge arises from existing recommender systems relying on factors like casting, title, and genre, using term frequency-inverse document frequency (TF-IDF) for vectorization, which prioritizes word frequency over semantic meaning. To address this, an innovative recommender system considering not only casting, title, and genre but also the short description of movies or shows is proposed in this study. Leveraging Word2Vec embedding for semantic relationships, this system offers recommendations aligning better with user preferences. Evaluation metrics including precision, mean average precision (MAP), discounted cumulative gain (DCG), and ideal cumulative gain (IDCG) demonstrate the system’s effectiveness, achieving a normalized DCG (NDCG)@10 of 0.956. A/B testing shows an improved click-through rate (CTR) of recommendations, showcasing enhanced streaming experience.
Volume: 36
Issue: 2
Page: 1218-1225
Publish at: 2024-11-01

Cost-effective circularly polarized MIMO antenna for Wi-Fi applications

10.11591/ijeecs.v36.i2.pp785-792
Raju Thommandru , Rengarasu Saravanakumar
Antenna is a backbone of communication system, and with the advent of technology, numerous innovations have been made to advance antenna development. An antenna, functioning as a smart device, transmits and receives signals while also working as a transducer. Wireless communication requires a useful device for transmitting and receiving electromagnetic waves. Wireless fidelity (Wi-Fi) is a specific type of wireless communication technology used to transmit data over the internet network. The bandwidth and signal coverage of Wi-Fi have significant limitations. Therefore, an antenna is crucial for improving signal reception to address this issue. This article presents the designing and developing of a cost-effective circularly polarized (CP) 2×2 multiple input multiple output (MIMO) antenna customized for Wi-Fi applications. The application of a notched circular patch antenna serves to achieve circular polarization. The radius of the circular patch is 0.26 λ, and the proposed MIMO antenna effectively showcases CP, characterized by an axial ratio (AR) of 1 dB at 5 GHz and an impressive bandwidth spanning 0.2 GHz (4.9-5.1 GHz). Additionally, the antenna is designed to achieve a high-isolation 2×2 MIMO setup, ensuring antenna isolation surpassing 20 dB. By utilizing the flame retardant (FR4) substrate, presented MIMO antenna strikes a balance between cost-effectiveness and operational efficiency for its intended application, and directional radiation patterns are well-aligned within the desired frequency range.
Volume: 36
Issue: 2
Page: 785-792
Publish at: 2024-11-01

Pavement health 4.0: a novel AI-enabled PavementVision approach for pavement health monitoring and classification

10.11591/ijeecs.v36.i2.pp1163-1171
Jaykumar Soni , Rajesh Gujar , Mohammed Shakil Malek
To determine the extent of pavement damage and forms of pavement distress, road pavement conditions must be precisely assessed. As a result, monitoring systems are regarded as an important stage in the maintenance procedure. In recent times, numerous investigations have been carried out to track the condition of pavement and monitor road surfaces. In the undertaken study, we have proposed a novel artificial intelligent (AI) and computer vision-enabled PavementCarevision 4.0 approach to detect and classify pavement health conditions i.e., defects. In this study, a customized pavement-2000 dataset has been designed which contains more than 2,000 images of a variety of pavement defects. In the initial phase, we pre-processed and enhanced pavement images using the customized adjustable linear contrast enhancement methodology. The enhanced pavement image samples were fed to the proposed customized YOLOV8 enabled PavementHealth 4.0 framework for pavement condition detection of a variety of pavement defects such as longitudinal cracks, alligator cracks, transverse cracks, and potholes. The proposed customized YOLOV8 enabled PavementHealth 4.0 framework has achieved an accuracy of 99.20 percent; an receiver operating characteristic (ROC) value of 0.98 and outperformed existing AI-based state-of-the-art methodologies such as pose NET, YOLOv7, YOLOv5, long short-term memory network (LSTM), Mask region-based convolutional neural network (R-CNN), and decision tree.
Volume: 36
Issue: 2
Page: 1163-1171
Publish at: 2024-11-01

Enhancing phishing URL detection through comprehensive feature selection: a comparative analysis across diverse datasets

10.11591/ijeecs.v36.i2.pp1182-1188
Preeti Preeti , Priti Sharma
Malicious attacks have developed a prominent risk to the safety of online users, with attackers employing increasingly sophisticated systems to deceive unsuspecting victims. This research focuses on the critical aspect of feature selection in optimizing phishing uniform resource locator (URL) detection system. Feature selection boosts machine learning (ML) and deep learning (DL) by picking vital attributes efficiently. This research paper provides a comprehensive examination of feature selection techniques using five diverse datasets. Various methods, including random forest (RF) select from model, SelectKBest with chi-square statistic, principal component analysis (PCA) and recursive feature elimination (RFE), were employed. The experiments, with a particular emphasis on PCA and fourth dataset, revealed that all four models RF, decision trees (DTs), XGBoost, and multilayer perceptron) achieved 100% accuracy in detecting phishing URL attacks. This underscores the efficacy of feature selection methods in enhancing to a deeper understanding of feature selection’s role in bolstering the effectiveness of phishing detection system across diverse datasets, highlighting the importance of leveraging techniques such as PCA for optimal results.
Volume: 36
Issue: 2
Page: 1182-1188
Publish at: 2024-11-01

Improved automated parallel implementation of GMM background subtraction on a multicore digital signal processor

10.11591/ijres.v13.i3.pp552-559
Smail Bariko , Abdessamad Klilou , Abdelouahed Abounada , Assia Arsalane
Scene segmentation is an essential step in a wide range of video processing applications, for instance, object recognition and tracking. The Gaussian mixture model (GMM) for background subtraction (BS) has gained widespread usage in scene segmentation, despite its known computational intensity. To tackle this challenge, we propose a practical solution to accelerate processing through a parallel implementation on an embedded multicore platform. In this paper, we present an improved automated parallel implementation of the GMM algorithm using the Orphan directive provided by open multiprocessing (OpenMP). Experimental assessments conducted on the eight cores of the C6678 digital signal processor (DSP) demonstrate significant advancements in parallel efficiency, particularly when handling high-resolution frames, including high-definition (HD) and full-HD resolutions. The achieved parallel efficiency surpasses the results obtained with classical OpenMP scheduling modes, encompassing dynamic, static, and guided approaches. Specifically, the parallel efficiency reaches approximately 82% for full-HD resolution frames and, 99.3% for low-resolution frames, respectively.
Volume: 13
Issue: 3
Page: 552-559
Publish at: 2024-11-01
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