cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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
 832   833   834   835   836 
Automatic kidney disease prediction using deep learning techniques Rubia, Jency; Shibi, Sherin; Lincy, Babitha; Catherin, Jenifer Pon; Vigneshwaran, Vigneshwaran; Nithila, Ezhil
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.pp1798-1806

Abstract

The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection.
A particle swarm optimization inspired global and local stability driven predictive load balancing strategy Dey, Niladri Sekhar; Raju Sangaraju, Hrushi Kesava
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.pp1688-1701

Abstract

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.
Performance evaluation of rank attack impact on routing protocol in low-power and lossy networks Al-Qaisi, Laila; Hassan, Suhaidi; Zakaria, Nur Haryani
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.pp242-251

Abstract

The internet of things (IoT) is a network of connected devices, enabling the exchange and collection of data from various environments. The routing protocol for low power and lossy networks (RPL) is a protocol for routing IPv6 over low-power wireless personal area networks, commonly used in IoT applications. However, RPL has several security and privacy issues that make it vulnerable to various attacks, including rank attacks (RA), which can lead to denial-of-service (DoS) scenarios. This research aims to address the impact of RA on RPL networks by conducting simulations using the Contiki/Cooja simulator with two topology types, random and grid, along with three RA scenarios and a normal network scenario. The study compares the performance of RPL network OF0 and MRHOF in terms of throughput, packet delivery ratio (PDR), hop count (HC) and delay. The results demonstrate that RA significantly degrades network performance and reduces network lifetime, thus draining its limited resources. Some possible solutions are also suggested to mitigate these attacks by focusing on core components of the network like objective function (OF) and node behavior. Future work will focus on studying security mechanisms for RPL against RA.
An interactive visualization tool for the exploration and analysis of multivariate ocean data K. G., Preetha; S., Saritha; Jeevan, Jishnu; Sachidanandan, Chinnu; Maheswaran, P. A.
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.pp1329-1337

Abstract

Ocean data exhibits great heterogeneity from variances in measuring methods, formats, and quality, making it extremely complicated and diverse due to a variety of data kinds, sources, and study elements. A few examples of data sources are satellites, buoys, ships, self-driving cars, and distant systems. The processing of data is made more challenging by the significant regional and temporal variations in oceanic characteristics including temperature, salinity, and currents. This work presents an interactive tool for multivariate ocean parameter visualisation, specifically overlays, based on Python. In ocean data visualisation, overlays are extra visual layers or data points that are layered to improve comprehension over a basic map. Based on the available data and the visualisation goals, these overlays are chosen and blended. Users can customise overlays with this tool, which also supports formatting, 2D and 3D visualisation, and data preparation. In order to reduce artefacts, it uses kriging interpolation for 3D visualisation and a modified version of the ray casting algorithm for representing octree data. By integrating overlays like as bathymetry, currents, temperature, and marine life, users can produce visually appealing and comprehensive depictions of ocean data. This method provides a thorough grasp of intricate marine processes by making it easier to see patterns, trends, and abnormalities in the data.
Optimizing dialog policy with large action spaces using deep reinforcement learning Thakkar, Manisha; Pise, Nitin
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.pp428-440

Abstract

Dialogue policy is responsible to select the next appropriate action from the current dialogue state to accomplish the user goal efficiently. Present commercial task-oriented dialogue systems are mostly rule-based; thus, they are not easily scalable to adapt multiple domains. To design an adaptive dialogue policy, user feedback is an essential parameter. Recently, deep reinforcement learning algorithms have been popularly applied to such problems. However, managing large state-action space is time consuming and computationally expensive. Additionally, it requires good quality and a reliable user simulator to train the dialogue policy which takes additional design efforts. In this paper, we propose a novel approach to improve the performance of dialogue policy by accelerating the training process by using imitation learning for deep reinforcement learning. We utilized proximal policy optimization (PPO) algorithm to model dialogue policy using a large-scale multi-domain tourist dataset MultiWOZ2.1. We observed a remarkable performance of dialogue policy with 91.8% task success rate, and an approximate 50% decrease in the average number of turns required to complete tasks without using user simulator in the early phase of training cycles. This approach is expected to help researchers to design computationally efficient and scalable dialogue agents by avoiding training from scratch.
Outage analysis of a single-threshold hard-switching hybrid FSO/RF system for reliable pico-macrocell backhauling Kassim, Abduljalal Yusha’u; Oduol, Vitalice Kalecha; Usman, Aliyu Danjuma
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.pp1543-1554

Abstract

In the quest for high-speed, reliable and cost-effective backhaul solutions for modern cellular networks, the hybrid free space optical (FSO) and radio frequency (RF) communication system is envisaged to be a promising technology. The hybrid system merges the benefits of both RF and FSO subsystems, delivering high data rates and reliability. The integration of both technologies improves the communication system's performance by addressing the inherent limitations of each. This study proposes a single-threshold hard-switching hybrid FSO/RF system for reliable pico-macrocell backhauling applications. We formulated closed-form expressions for the cumulative density functions (CDFs), probability density functions (PDFs), and outage probability (OP) for RF-only, FSO-only and hybrid FSO/RF links. The rician fading and gamma-gamma (G-G) channel distributions were utilized, respectively. The average received signal-to-noise ratio (SNR) determines the switching mechanism based on the defined threshold and atmospheric condition. Simulation results and analysis demonstrated that, at any average SNR above the defined threshold, the hybrid system’s OP outperforms that of the RF-only and FSO-only links under most conditions. The analysis illustrates that employing the hybrid FSO/RF system enhances reliability and boosts overall system performance in pico-macrocell backhauling scenarios, surpassing the performance of standalone FSO-only or RF-only links.
User self-efficacy enhances business intelligence tools for organizational agility Al-Dwairi, Radwan Moh’d; Al-Khataybeh, Maali; Najadat, Dania; Rawashdeh, Adnan
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.pp592-602

Abstract

The primary objective of this paper is to investigate the interplay between individual self-efficacy (SE) and the adoption of business intelligence (BI) tools, and their combined effects on organizational agility and performance. This research offers a novel perspective by examining the relationship between individual SE and BI tools together, which was neglected in the previous research, shedding light on how these factors collectively influence organizational performance and agility. The importance of this study addresses the crucial need for understanding the role of individual capabilities in leveraging BI tools, especially in the context of rapidly changing environments. The study employs a quantitative approach to examine the proposed model. A survey was conducted with 174 respondents from private and public organizations in Jordan. The findings reveal significant and positive impacts of individual experiences, vicarious experiences (VE), and psychological feedback (PS) on SE. Moreover, the study demonstrates that SE significantly and positively influences the utilization of BI tools, consequently affecting organizational agility and performance. The significance of the study findings lies in its ability to bridge the gap between individual capabilities and the effective utilization of BI tools to equip businesses with invaluable insights for enhancing their decision-making processes.
Notice of Retraction Design of mean filter using field programmable gate arrays for digital images Ai, Duong Huu; Nguyen, Van Loi; Luong, Khanh Ty; Le, Viet Truong
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.pp1430-1436

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.The presenting author of this paper has the option to appeal this decision by contacting ijeecs.iaes@gmail.com.-----------------------------------------------------------------------In this paper, we design and analysis of mean filter using field programmable gate arrays (FPGAs) for digital images, FPGAs are integrated circuits consisting of interconnections that connect programmable internal hardware blocks allows users to customize operations for a specific application. FPGA is an ideal choice for real-time image processing, these FPGA devices are controlled in Verilog or VHDL languages, allowing to design at different levels and adapt to design changes or even support new applications throughout the life of the component. Digital image filtering is the most important task in image processing and with the help of computers, image recognition involves identifying and classifying objects in an image. This paper design of mean filter for digital image processing, implementation and analysis of image processing algorithms on FPGAs. The results obtained on the FPGA are compared and analyzed with the results by MATLAB software.
Combination certainty factor method and fuzzy expert system module to determine the dose of leukemia drugs Krisbiantoro, Dwi; Wanti, Linda Perdana; Adi Prasetya, Nur Wachid
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.pp1915-1923

Abstract

Leukemia is a type of blood cancer. Treatment for leukemia patients can last for years because the dose of medication given is adjusted to the patient's immune system. The aim of this research is the use of information technology through a combination of certainty factors and the development of a fuzzy expert system (FES) module to determine the therapeutic schedule for administering leukemia drugs. The urgency of this research is to help medical personnel in measuring the dose of leukemia medication to be given to patients so as to increase the cure rate for leukemia patients. The method used is certainty factor and fuzzy logic. The combination of the certainty factor method and the FES module which is carried out using input variables in the form of the severity of the leukemia suffered by the patient is to produce an appropriate therapeutic schedule for administering leukemia drugs. The result of this research is a combination of the factor certainty method and the FES module which has been tested and the accuracy level is 95.17%, the same as recommendations from experts.
Microarray classification using genetic algorithm and latin hypercube sampling Awangditama, Bangun Rizki; Suciati, Nanik
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.pp1976-1985

Abstract

Cancer, the second leading cause of global death, requires advanced diagnostic technology. Microarray gene expression technology plays an important role in comprehensively analyzing the genetic aspects of cancer. However, challenges such as high-dimensional attributes, limited samples, and varying gene presence rates hinder the accurate classification of microarray data. This study proposes a model that uses latin hypercube sampling (LHS) in genetic algorithms (GA) for Feature Selection in microarray data classification. LHS makes the chromosome samples in the initial population of GAs representative and diverse. The study used three microarray datasets with different numbers of features and classes. The results reveal that first, the use of GA alone tends to limit the exploration of the resulting feature space, while the use of LHS can expand the feature selection possibilities in the context of feature selection. Secondly, this study shows that microarray classification using GA with LHS (GALHS) consistently outperforms other feature selection methods such as based correlation features (BCF), principal component analysis (PCA), relief, and lasso. Thus, this research contributes to feature selection by applying LHS and GA to optimize the performance of microarray data classification models.

Page 834 of 914 | Total Record : 9138

 832   833   834   835   836 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue