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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Notice of Retraction An NFMF-DBiLSTM model for human anomaly detection system in surveillance videos Angadi, Sanjeevkumar; Moorthy, Chellapilla V. K. N. S. N.; Tripathi, Mukesh Kumar; Tingare, Bhagyashree Ashok; Kadam, Sandeep Uddhavrao; Misal, Kapil
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp647-656

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.-----------------------------------------------------------------------In response to the increasing demand for an intelligent system to avoid abnormal events, many models for detecting and locating anomalous behaviors in surveillance videos have been proposed. Nevertheless, significant flaws of inadequate discriminating ability are present in the majority of these models. A novel newton form and monotonic function based deep bidirectional long short-term memory (NFMF-DBiLSTM) human anomaly recognition system was discussed in this paper to tackle those issues. Initially, videos are transformed into frames; after that, the duplicate frames are removed, and by utilizing the shannon entropy centered contrast limited adaptive histogram equalization (SE-CLAHE) algorithm, the contrast has been elevated. By using the probabilistic matrix factorization kernel density estimation (PMF-KDE) technique, the background is subtracted after estimating only the motion of the object. After this, the silhouette function is performed utilizing the dirac depth silhouette function (DDSF). In addition, clustering is done by sorting and average-based K-means (SA-KM). The features are extracted from the suspected human and are then chosen by utilizing Poisson Eurasian oystercatcher optimization (PEOO). For classifying normal or anomaly, the selected features are subjected directly into the NFMF-DBiLSTM. When contrasted with the prevailing methodologies, the proposed model is found to be more efficient.
Developed improved lion optimization for breast cancer classification using histopathology images Ali Khan, Pattan M. D.; Rathina, Xavier Arputha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1613-1619

Abstract

Breast cancer, a prevalent kind of cancer, is a major health problem among women. Researchers recently achieved categorization effectiveness of breast cancer (BC) detection in histopathology picture database using convolutional neural networks (CNNs) of medical image processing. Although CNN method parameter settings were complex, employing breast cancer histopathological database (BCHD) data for categorization was valued as expensive. This research used uniform experimental design (UED) to solve these issues and improved lion optimization (ILO) breast cancer histopathology image categorization. To optimize the variables at UED-ILO, a regression method was employed. According to the experimental data, the proposed approach of UED-ILO (uniform experimental design based improved lion optimization) variable optimization provided a categorization accuracy rate of 84.41%. Finally, the proposed approach can effectively increase classification accuracy, with results that outperform others of an equivalent nature.
MLFF-Net: a multi-model late feature fusion network for skin disease classification Gairola, Ajay Krishan; Kumar, Vidit; Sahoo, Ashok Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1906-1914

Abstract

Early diagnosis is paramount to preventing skin diseases and reducing mortality, given their global prevalence. Visual detection by experts using dermoscopy images has become the gold standard for detecting skin cancer. However, a significant challenge in skin cancer detection and classification lies in the similarity of appearance among skin disease lesions and the complexity of dermoscopic images. In response, we developed multi-model late feature fusion network (MLFF-Net), a multi-model late feature fusion network tailored for skin disease detection. Our approach begins with image pre-processing techniques to enhance image quality. We then employ a two-stream network comprising an enhanced densely linked network (DenseNet-121) and a vision transformer (ViTb16). We leverage shallow and deep feature fusion, late fusion, and an attention module to enhance the model’s feature extraction efficiency. The subsequent feature fusion module constructs multi-receptive fields to capture disease information across various scales and uses generalized mean pooling (GeM) pooling to reduce the spatial dimensions of lesion characteristics. Finally, we implement and test our skin lesion categorization model, demonstrating its effectiveness. Despite the combination, convolutional neural network (CNN) outperforms ViT approaches, with our model enhancing the accuracy of the best model by 6.1%.
Design of energy efficient and reconfigurable sample rate converter using FPGA devices Pinjerla, Swetha; Rao, Surampudi Srinivasa; Reddy, Puttha Chandrasekhar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp854-862

Abstract

The technique of sampling rate conversion is frequently employed in various fields. A discrete time-varying filter, as well as a sample skip or sample duplicate operation, are required for the most general instance of an irrational and time-variable conversion factor. A wide band of signals is employed in a communication system, especially in specific situations where data must be transferred directly. A broadband sample rate converter with changeable filter parameters is necessary in such cases. Sample rate conversion is a communication system technology that accepts a band-limited high sample rate modulated signal and uses filtering to retrieve the original message signal. In this work, an energy-efficient implementation of a reconfigurable field programmable gate arrays (FPGA) architecture for a sample rate converter is proposed. In applications such as multi-rate signal processing and the construction of channelized receivers, sample rate conversion is used. In this work, a new FPGA based design is proposed to perform multiple sample rate conversion for various data transmission protocols such as Wi-Fi, ZigBee and Bluetooth. A lowpass filter with a 2.45 GHz filter with the minimum number of taps is used to avoid the aliasing effect. Xilinx synthesis tools are used to estimate hardware resource utilization and speed analyses. XC6VCX240t-2FF484 FPGA achieves 15% hardware resource occupancy at a maximum clock speed of 133 MHz.
Minitab 20 and Python based-the forecasting of demand and optimal inventory of liquid aluminum sulfate supplies Dwipurwani, Oki; Puspita, Fitri Maya; Supadi, Siti Suzlin; Yuliza, Evi
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1796-1807

Abstract

In a company, inventory management is crucial due to the significant impact on various aspects of the business. Similarly, the Indonesian water supply company (PDAM) requires effective inventory management to ensure the supply of liquid aluminum sulfate chemicals. The probabilistic statistical inventory control (SIC) model is commonly used for inventory management. However, previous research on chemical inventory models in PDAMs often relied on simple linear regression to forecast demand data, which fails to capture the inherent volatility in demand. Therefore, this research aimed to predict demand data using the seasonal autoregressive integrated moving average (SARIMA) method and determine the optimal policy for supplying liquid aluminum sulfate chemicals. The results showed that the best demand forecasting model was SARIMA (2,1,2) (1,1,0)12 with a mean absolute percentage error (MAPE) value of 8.19%. The finding of the optimal inventory policy showed a safety stock value of 11,922.35 kg, a reorder point value of 49,511.20 kg, and an order quantity of 21,526.59 kg, leading to a total cost of IDR 11,132,034,145.45. The sensitivity test also showed that variations in lead time, price, μ, and σ parameters directly influence changes in total cost, reorder point, and safety stock. These calculations were conducted using Minitab and Python software.
Effects of TiO2 in graphene-quantum-dot film on lighting color uniformity of a white light-emitting diodes Le, Phan Xuan; Cong, Pham Hong
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp800-807

Abstract

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.
A framework for reusable domain specific software component extraction based on demand Basha, N Md Jubair; Ganapathy, Gopinath; Mohammed, Moulana
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp274-281

Abstract

The majority of organizations use an agile software development methodology. Standard analysis and design processes are abandoned due to the enormous demand of generating the product within time and budget. This may result in a lack of high-quality software while components are not constructively reused. The components are identified at a later stage in the majority of component approaches. To address such challenges, a methodology for extracting demand-based domain-specific software components from the repository was developed. The process for reusing current components is described in depth with various domain-specific components, and the suggested framework is for extracting demand-based reusable domain-specific software components.
Hybrid logistic regression support vector model to enhance prediction of bipolar disorder Agnihotri, Nisha; Prasad, Sanjeev Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1294-1300

Abstract

Bipolar disorder has become one of the major mental health issues due to stressed life around the world. This is the major reason for suicides these days as these people are unable to convey their feeling and emotions to others. This proposed research shows the logistic regression and support vector machine hybrid model to predict bipolar disorder in patients is to develop an accurate and reliable model that can effectively predict the presence of bipolar disorder in patients based on their clinical and demographic data. The purpose is to make a framework that can help healthcare professionals diagnose bipolar disorder early, thereby enabling timely and appropriate treatment to be provided. The model should take into account various patient-specific features, such as age, gender, family history, medication use, and other medical conditions, in addition to relevant clinical and demographic variables. The aim is to create a model that can accurately classify patients with bipolar disorder and non-bipolar disorder patients while minimizing false-positive and false-negative classifications. The work shows improvement in evaluation detection in performance with hybrid logistic support vector regression (LSVR) to detect disorder and protect them to avoid worse situation.
Hybrid optimized multi-objective honey badger algorithm and NSGA-II for feature selection problems Papasani, Anusha; Devarakonda, Nagaraju
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp493-500

Abstract

One of the most important aspects of classification is choosing features in such a way as to get rid of redundant or irrelevant elements in the dataset. For the most part, multi-objective feature selection strategies have been offered by a number of scholars as a strategy for this aim. On the other hand, these techniques frequently fail to simultaneously improve classification accuracy while removing redundant feature combinations. This article presents a wrapper-based feature selection strategy that strikes a compromise between classification accuracy and redundancy reduction by combining features of the multi objective (MO) based honey badger algorithm (MO-HBA) and non-dominated sorting genetic algorithm-II (NSGA-II). The technique was developed as part of this investigation. Increasing the accuracy of the classification while simultaneously reducing the number of redundant characteristics is one of the optimizations aims of this approach. The MO-HBA shows excellent performance in exploration and exploitation. A Kernel version of the extreme learning machine (KELM) is used for the process of selecting the features to use. In order to evaluate how well this method of feature selection performs, eighteen benchmark datasets are utilized, and the results are compared to four established methods of multi-objective feature selection based on different metrics.
Improve fractal interpolation function with Sierspinski triangle Susanti, Eka; Puspita, Fitri Maya; Supadi, Siti Suzlin; Yuliza, Evi; Fadhila Chaniago, Redina An
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1485-1492

Abstract

Interpolation techniques can be used to determine the approximate value of a parameter if it is known that two values are bound to a certain interval. Interpolation can be done numerically or fractal. The fractal interpolation value is influenced by the vertical scale factor and the fractal interpolation function (FIF). This research introduces fractal interpolation technique with FIF which is constructed from Sierspinski triangles. As an example of application, the interpolation technique is applied to determine the approximate value of the rice demand parameter in the inventory model. The accuracy of the interpolation results is determined using the mean absolute percentage error (MAPE). The number of triangles obtained and the interpolation values for each successive iteration are 3???? and 3????+1. MAPE values from 6 to 9 iteration were 24.603%, 24.603%, 23.858%, 23.772% respectively. There is a decrease in the value of MAPE, this indicates an increase in the value of the accuracy of the interpolation results. It can be concluded that the MAPE value is also influenced by the number of iterations of the interpolation technique.

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