Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

28,188 Article Results

Design of a segmented current steering digital to analog converter using PMOS cascode current source in UMC 65 nm technology

10.11591/ijeecs.v39.i2.pp821-830
Ashok Kumar Adepu , Balaji Narayanam
Digital to analog converters (DAC) are the fundamental data converters used in the digital data transmission. In this paper 8-bit DAC is proposed using current cells with LSB current of 2 µA and full-scale voltage of 420 mV. Current cells mean the current sources designed using the MOSFETs. When it comes to mixed-signal and analog integrated circuits, current cells are the fundamental building blocks that are available. The optimized performance of current source is obtained with the proper biasing circuit. The performance of these current mirrors is evaluated in terms of key parameters such as output impedance, transconductance and linearity. The simulations for testing these parameters are performed using Virtuoso Cadence tool in umc 65 nm technology. After transistor characterization, various types of current sources are designed, and for each current mirror, PVT analysis is carried out for comparison and Monte-Carlo analysis is carried out to find the mismatch in current mirrors. Then different digital blocks are designed, that are D-Latch, Binary-thermo decoder and Row-column decoder which are required for designing of current steering DAC. Creating an 8-bit segmented current steering DAC by combining the ideas of 4-bit unary weighted and 4-bit binary-weighted DACs is the aim of this research. Performance measurements such as signal-to-noise ratio (SNR), effective number of bits (ENOB), spurious-free dynamic range (SFDR), differential non-linearity (DNL), and integral non-linearity (INL) are calculated to assess the proposed 8-bit segmented DAC. The analysis and designing of current mirrors in advanced CMOS technologies are critical for the development of high performance integrated circuits. An 8-bit DAC implemented using ILSB current of 2 µA with an accuracy of ±2%.
Volume: 39
Issue: 2
Page: 821-830
Publish at: 2025-08-01

Wolfram Alpha based-inventory model for damaged items of pharmaceutics by utilizing exponential demand rate

10.11591/ijeecs.v39.i2.pp1145-1154
Indrawati Indrawati , Fitri Maya Puspita , Siti Suzlin Supadi , Evi Yuliza , Farah Nabilah Tampubolon
In this study, an inventory model is developed for pharmaceutical products that deteriorate over time with an exponential demand rate. The discussion of exponential demand is rarely explored but has the advantage that the demand value toward total cost remains positive. This study assumes allowable shortages and complete backlogging, making it necessary to design an optimal policy for deteriorating goods with an exponential demand rate. The model shows that the initial stock decreases over time, potentially leading to shortages before the next order arrives. The optimal solution indicates that the inventory reaches the zero point at 𝑡1 = 0.0000011 and the cycle length 𝑇1 = 0.012 resulting in an average minimum total cost of 𝑇𝐶̅̅̅̅ = $17,133.9 per cycle by Wolfram Alpha. Sensitivity analysis measures the changes of the results in the increasing value of 𝑇𝐶̅̅̅̅ for all parameters. Exponential function variables (𝑎 and 𝑏) produces 𝑡1 and 𝑇1 stable values. On increasing the cost of each damage (𝐷𝐶) and constant damage rate (𝜃) produces a 𝑡1 stable value, but the value of 𝑇1 increases. An increase in storage costs (h) results in a decrease in the value of 𝑡1 and 𝑇1. Increasing in the cost of shortages (s) resulted in an increase in the value of 𝑡1 and a decrease in the value of 𝑇1.
Volume: 39
Issue: 2
Page: 1145-1154
Publish at: 2025-08-01

Development of mobile-based Batak script recognition application using YOLOv8 algorithm

10.11591/ijeecs.v39.i2.pp1013-1026
Iustisia Natalia Simbolon , Herimanto Herimanto , Ranty Deviana Siahaan , Samuel Adika Lumbantobing , Grace Natalia Br Sitepu
The Batak people are one of the ethnic groups that pass down many values and traditions to each generation, including the written tradition known as the Batak script. The Batak Toba people, in particular, have the Batak Toba script as part of their local wisdom that needs to be preserved and maintained. However, the use of the Batak script has significantly declined in the current era. To prevent the loss of this heritage, preservation through technology is necessary. This research utilizes a deep learning approach using the YOLOv8 algorithm to detect images of script objects, provide the coordinates of the script locations, and perform object recognition based on the dataset. The final result of this research is an Android-based application that can detect the Batak Toba script in real time and upload images. The research process involves experiments on several hyperparameters, such as epochs with a value of 200, confidence threshold, and IoU with a value of 0.5. The model evaluation shows excellent results, with a precision of 0.945, recall of 0.902, mAP@0.5 of 0.954, and a high confidence score from the application's detection.
Volume: 39
Issue: 2
Page: 1013-1026
Publish at: 2025-08-01

Processing queries on encrypted document-based database

10.11591/ijeecs.v39.i2.pp1299-1309
Abdelilah Belhaj , Soumia Ziti , Karim Elbouchti , Noureddine Falih , Souad Najoua Lagmiri
Big  data is a set of technologies and strategies for storing and analyzing large volumes of data in order to learn from it and make predictions. Since non-relational databases such as document-based have been applied in various contexts, the privacy protection must be taken into account by strengthening security to prevent the exposure of user data. In this paper, we focus mainly on secret sharing scheme that supports secure query with data interoperability to design a practical model for document-based databases, especially MongoDB. This approach, being based on secure query processing by defining elementary and suitable operators, allows us to perform operational computations and aggregations on encrypted data in the non-relational document database MongoDB. The obtained results, in the present work, could find places in various fields where data privacy and security are primordial such as healthcare, cloud computing, financial services, artificial intelligence and machine learning, in which user data remains secure and confidential during processing.
Volume: 39
Issue: 2
Page: 1299-1309
Publish at: 2025-08-01

Dynamic attendance system using face recognition via machine learning models

10.11591/ijeecs.v39.i2.pp1421-1430
Nishant Upadhyay , Nidhi Bansal , Emil Velinov , Harshit Harshit , Abhay Sharma , Sanjeev Kumar
Traditional methods to handle attendance have been implemented in the schools in the past and most of them are discouraging as they require that the institutions implement the use of paper and pen to get the results. To enhancing effectiveness and safeguarding, this paper presents a face recognition attendance system that mechanizes the usual attendance taking process. Using best practices in facial recognition, the system captures images of students’ faces, stores them, feeds them into a recognition model, and uses real-time facial recognition to mark attendance. This means that the system enjoys data encryption and password protected access that ensures data is safe. In the proposed system, the OpenCV face recognition libraries combined with machine learning algorithms for better face recognition ability with better efficiency. The results confirm that the system provides a reliable approach to handling attendance and it may debut in various contexts.
Volume: 39
Issue: 2
Page: 1421-1430
Publish at: 2025-08-01

Clustering technique for dense D2D communication in RIS-aided multicell cellular network

10.11591/ijeecs.v39.i2.pp927-940
Misfa Susanto , Soraida Sabella , Lukmanul Hakim , Rudi Kurnianto , Azrina Abd Aziz
Device-to-device (D2D) communication and reconfigurable intelligent surface (RIS) are well-known as two promising technologies for nextgeneration cellular communication networks. D2D users operate on the same spectrum as traditional cellular users, potentially leading to increased interference and reduced efficiency in frequency resource usage. RIS provides a remedy for clearing blocked signals from obstructions by reflecting the desired signals to the intended receiver. However, RIS elements reflect not only the desired signals but also the interference signals. This paper proposes a distance-based clustering method aimed at creating a grouping algorithm for neighboring D2D users using different channels, thereby reducing co-channel interference. The simulation indicates that the proposed clustering method for D2D users' equipment (DUEs) leads to a 0.72 dB increase in signal-to-interference-plus-noise ratio (SINR), enhances throughput to 11.25 Mbps, and reduces the bit error rate by up to 24×10⁻² compared to the baseline system. The study findings also indicate that cellular users' equipment (CUEs) experience satisfactory signal quality, even with the presence of DUEs on the cellular network. Our clustering algorithm is feasible to deploying D2D densely in RIS-aided cellular network without significantly affecting CUE performance.
Volume: 39
Issue: 2
Page: 927-940
Publish at: 2025-08-01

Identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering

10.11591/ijeecs.v39.i2.pp1100-1108
Shiny Rajendrakumar , Rajashekarappa Rajashekarappa , Vasudev K. Parvati
Plant disease diagnosis is crucial for preventing productivity and quality losses in agricultural products. Because plants are continually attacked by insects, bacterial infections, and smaller scale organisms it is necessary for early diagnosis disease control is a vital part of profitable chilli crop production, hence early diagnosis of disease identification is an important aspect of crop management. This paper discusses strategies for detecting disease effectively in order to improve chilli plant product quality. An image processing technique based on identification of chilli leaf disease using contrast limited histogram equalisation and k-means clustering (KMC). The approach was carried out in five stages: acquiring the image, preprocessing, extracting features, classifying the diseases, and showing the outcome. This work offers a thorough implementation of CLAHE for preprocessing, k-means cluster for feature extraction and support vector machine (SVM) for classification of chilli leaf diseases. The accuracy was tested for standard chilli dataset for major 2 types of diseases including anthracnose and bacterial blight form kaggle dataset with varying samples of 70:30 and 60:40 respectively and it is observed that the average accuracy improved to 98% compared to existing techniques.
Volume: 39
Issue: 2
Page: 1100-1108
Publish at: 2025-08-01

Recognizing AlMuezzin and his Maqam using deep learning approach

10.11591/ijeecs.v39.i2.pp1360-1372
Nahlah Mohammad Shatnawi , Khalid M. O. Nahar , Suhad Al-Issa , Enas Ahmad Alikhashashneh
Speech recognition is an important topic in deep learning, especially to Arabic language in an attempt to recognize Arabic speech, due to the difficulty of applying it because of the nature of the Arabic language, its frequent overlap, and the lack of available sources, and some other limitations related to the programming matters. This paper attempts to reduce the gap that exists between speech recognition and the Arabic language and attempts to address it through deep learning. In this paper, the focus is on Call for Prayer (Aladhan: ناذآلا ) as one of the most famous Arabic words, where its form is stable, but it differs in the notes and shape of its sound, which is known as the phonetic Maqam (Maqam: ماقملا  يتوصلا ). In this paper, a solution to identify the voice of AlMuezzin ( نذؤملا ), recognize AlMuezzin, and determine the form of the Maqam through VGG-16 model presented. The VGG-16 model examined with 4 extracted features: Chroma feature, LogFbank feature, MFCC feature, and spectral centroids. The best result obtained was with chroma features, where the accuracy of Aladhan recognition reached 96%. On the other hand, the classification of Maqam with the highest accuracy reached of 95% using spectral centroids feature.
Volume: 39
Issue: 2
Page: 1360-1372
Publish at: 2025-08-01

Performance evaluation of a photovoltaic system with phase change material in Guwahati

10.11591/ijeecs.v39.i2.pp737-746
Pallavi Roy , Bani Kanta Talukdar
Recently, there has been a lot of interest in solar photovoltaic (PV) technology as a clean and renewable energy source. The operating temperature of PV modules significantly impacts their performance; as the temperature rises, the modules perform worse. The phase change material (PCM) paraffin wax has been used to cool a PV system passively. The experiment was carried out during summer over three months, viz. April, May, and June when relative humidity was around 80.75% to 86.5% with two identical 20-watt PV panels in Guwahati, India (26.1332° North and 91.6214° East). One panel was coated with PCM, while the other panel functioned as a point of reference. The study reveals an impressive result: the output power produced by the system with PCM was 9.8%, 13.1%, and 10.3% greater than the reference PV, while the surface temperature had been lowered by 21.6%, 26.2%, and 30.6% in the three respective months. High humidity delays the release of latent heat of paraffin wax and hence improves its thermal conductivity. This study adds to the continuing efforts to promote sustainable energy solutions and creates new opportunities to enhance the performance of PV systems.
Volume: 39
Issue: 2
Page: 737-746
Publish at: 2025-08-01

Optimization of IoT-based monitoring system for automatic power factor correction using PZEM-004T sensor

10.11591/ijeecs.v39.i2.pp860-873
Maman Somantri , Mochamad Rizal Fauzan , Irgi Surya
Power factor correction (PFC) is crucial for improving energy efficiency and reducing excessive power consumption, especially in inductive loads commonly found in household and industrial environments. Conventional PFC methods often rely on manual capacitor switching, which is inefficient and impractical for real-time applications. This study proposes an IoT-based automatic power factor monitoring and correction system that dynamically adjusts the power factor using real-time data analysis. The system integrates NodeMCU ESP32 and the PZEM-004T sensor to monitor electrical parameters and automatically switch capacitors based on power factor conditions. The research follows the ADDIE approach (analysis, design, development, implementation, evaluation) to ensure a structured development process. Experimental results demonstrate an average power factor improvement of 48.77% and a reduction in current consumption by 39.90%, significantly enhancing energy efficiency. The system's web-based interface allows real-time monitoring with an average data transmission response time of 207.67 ms, ensuring efficient remote management. Compared to existing systems, the proposed approach eliminates manual intervention and optimizes PFC adaptively. Future research should focus on expanding system reliability, testing on larger-scale applications, and integrating artificial intelligence (AI) for predictive power factor adjustments.
Volume: 39
Issue: 2
Page: 860-873
Publish at: 2025-08-01

A multi-tier framework of decentralized computing environment for precision agriculture (DCEPA)

10.11591/ijeecs.v39.i2.pp1072-1080
Kiran Muniswamy Panduranga , Roopashree Hejjaji Ranganathasharma
Although collecting enormous volumes of heterogeneous data from many sensors and guaranteeing real-time decision-making are problems, precision agriculture (PA) has emerged as a promising approach to increase agricultural efficiency. The efficacy of current centralized solutions is limited in large-scale agricultural settings due to resource limitations and data saturation. In order to solve these problems, this paper suggests a decentralized computing environment for precision agriculture (DECPA), which divides resource management and data processing among several layers (end, edge, and cloud). DECPA optimizes task execution and resource allocation in the field by utilizing ensemble machine learning models (deep neural network (DNN), long short-term memory (LSTM), autoencoder (AE), and support vector machine (SVM)) and a multi-tier architecture. The findings demonstrate that DECPA combined with DNN performs better than alternative models, achieving a 20% decrease in energy usage, an 18% speedup in response time, a 5% improvement in accuracy, and a 51% reduction in latency. This illustrates the system’s capacity to manage massive amounts of data effectively while preserving peak performance. To sum up, DECPA uses decentralized resources and cutting-edge machine learning models to provide a scalable and affordable precision agriculture solution. To improve the system’s flexibility and real-time responsiveness, future research will investigate additional optimization and use in various agricultural contexts.
Volume: 39
Issue: 2
Page: 1072-1080
Publish at: 2025-08-01

Hierarchical enhanced deep encoder-decoder for intrusion detection and classification in cloud IoT networks

10.11591/ijeecs.v39.i2.pp1176-1188
Ramya K. M. , Rajashekhar C. Biradar
Securing cloud-based internet of things (IoT) networks against intrusions and attacks is a significant challenge due to their complexity, scale, and the diverse nature of connected devices. IoT networks consist of billions of devices, computer servers, data transmission networks, and application computers, all communicating vast amounts of data that must adhere to various protocols. This study introduces a novel approach, termed hierarchical enhanced deep encoder-decoder with adaptive frequency decomposition (HED-EDFD), and is designed to address these challenges within cloud-based IoT environments. The HED-EDFD methodology integrates adaptive frequency decomposition, specifically adaptive frequency decomposition, with a deep encoder-decoder model. This integration allows for the extraction and utilization of frequency domain features from time-sequence IoT data. By decomposing data into multiresolution wavelet coefficients, the model captures both high-frequency transient changes and low-frequency trends, essential for detecting potential intrusions. The deep encoder-decoder model, enhanced with deep contextual attention mechanisms, processes these features to identify complex patterns indicative of malicious activities. The hierarchical structure of the approach includes a hierarchical wavelet-based attention mechanism, which enhances the accuracy and robustness of feature extraction and classification. To address the issue of imbalanced intrusion data, a cosine-based SoftMax classifier is employed, ensuring effective recognition of minority class samples.
Volume: 39
Issue: 2
Page: 1176-1188
Publish at: 2025-08-01

Analyzing and clustering students admission data in Yala Rajabhat University Thailand

10.11591/ijeecs.v39.i2.pp1310-1325
Thanakorn Pamutha , Wanchana Promthong , Sofwan Pahlawan
This research explores the use of clustering techniques to analyze student admission data at Yala Rajabhat University, Thailand, aiming to enhance recruitment strategies and understand student profiles. Employing K-means, Hierarchical Clustering, and Density-based spatial clustering of applications with noise (DBSCAN), the study groups admission data based on factors like educational institution, geographic location, and program chosen. The methodology incorporates normalization and principal component analysis (PCA) to ensure data quality, while the Elbow Method determines the optimal number of clusters for effective data segmentation. The davies-bouldin index (DBI) evaluates the clustering configurations, ensuring that clusters are well-separated and cohesive. The results reveal distinct student profiles that can inform targeted marketing and improve recruitment strategies. This study not only provides strategic insights into student recruitment but also contributes to the literature on the use of data science in educational settings, highlighting the transformative impact of advanced analytics on institutional effectiveness. The research emphasizes the importance of data-driven approaches in adapting to the changing dynamics of student admissions and the competitive landscape of higher education.
Volume: 39
Issue: 2
Page: 1310-1325
Publish at: 2025-08-01

Enhancing touchless smart locker systems through advanced facial recognition technology: a convolutional neural network model approach

10.11591/ijai.v14.i4.pp3262-3273
Abdul Haris Rangkuti , Evawaty Tanuar , Febriant Yapson , Felix Octavio Sijoatmodjo , Varyl Hasbi Athala
As the world recovers from COVID-19, demand for contactless systems is increasing, promising safety and convenience. Touchless technology, particularly public locker security systems that use facial recognition and hand detection, is advancing rapidly. The system minimizes physical contact, increasing user safety. It uses advanced models such as multi-task cascaded convolutional networks (MTCNN) and RetinaFace, FaceNet512, ArcFace, and visual geometry group (VGG)-Face for face detection and recognition, with a combination of RetinaFace, ArcFace, and L2 norm Euclidean or cosine as the most effective distance metric method, where the accuracy reaches 96 and 90%. 'Yourvault', an application demonstrating this efficient security feature, provides notifications for mask detection, facial authenticity and locker status, offering a solution to the problem of convenience and security of public spaces. Future research could investigate the impact of photo age on facial recognition accuracy, potentially making touchless systems more efficient. In general, the application of this technology is an important step towards a safer and more comfortable world after the pandemic. This model approach can be followed up with more optimal facial recognition.
Volume: 14
Issue: 4
Page: 3262-3273
Publish at: 2025-08-01

Comparing bidirectional encoder representations from transformers and sentence-BERT for automated resume screening

10.11591/ijai.v14.i4.pp3404-3411
Asmita Deshmukh , Anjali Raut Dahake
In today’s digital age, organizations face the daunting challenge of efficiently screening an overwhelming number of resumes for job openings. This study investigates the potential of two state-of-the-art natural language processing models, bidirectional encoder representations from transformers (BERT) and sentence-BERT (S-BERT), to automate and optimize the resume screening process. The research addresses the need for accurate, efficient, and unbiased candidate evaluation by leveraging the power of these transformer-based language models. A comprehensive comparison between BERT and S-BERT is performed, evaluating their performance across multiple metrics, including accuracy, screening time, correlation with job descriptions, and ranking quality. The findings reveal that S-BERT outperforms BERT, achieving higher accuracy (90% vs. 86%), faster screening time (0.061 seconds vs. 1 second per resume), and stronger correlation with job descriptions (0.383855 vs. 0.1249). S-BERT though has a smaller vector size of 384 enables capturing richer semantic information compared to BERT’s vector size of 768, contributing to its superior performance. The study provides insights into the strengths and limitations of each model, offering valuable guidance for organizations seeking to streamline their talent acquisition processes and enhance candidate selection through automated systems.
Volume: 14
Issue: 4
Page: 3404-3411
Publish at: 2025-08-01
1 2 3
...
1879 1880
Show 27 of 1880

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration