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31,456 Article Results

Probing the depths: assessing the efficacy of the two-tier deception-driven security model

10.11591/ijeecs.v36.i3.pp1631-1639
Anazel P. Gamilla , Thelma D. Palaoag , Marlon A. Naagas
In the age characterized by relentless cyber threats, the need for innovative and proactive security measures has never been more important. Deception is defined as the deliberate structure of tricks, traps, and false information to mislead and discourage threats, while providing timely warning signals and useful information to defenders. The two-tier deception-driven security model's implementation focuses on applying deception security techniques to deceive potential attackers and protect network resources, with an emphasis on a proactive defense approach. The study emphasized the deployment and deep testing of the model, which aims to assess its efficacy and feasibility in real-time practice. The study shows that the two-layered approach effectively defends the network within the multiple layers using a combination of decoys, honeypots, and deceptive network segments. The deception security model effectively prevents and confuses potential threats, improving the network's overall resilience and threat defense capabilities. The findings suggest that integrating deception techniques into cybersecurity frameworks can provide a robust layer of protection against evolving cyber threats. Furthermore, this research contributes to the ongoing discourse on proactive cybersecurity strategies and offers practical insights for improving network defense mechanisms.
Volume: 36
Issue: 3
Page: 1631-1639
Publish at: 2024-12-01

Advanced control and optimization strategies for a 2-phase interleaved boost converter

10.11591/ijeecs.v36.i3.pp1421-1429
Muhammad Adnan Samad , Usmonov Shukurillo Yulbarsovich , Sultonov Ruzimatjon Anvarjon Ugli , Saima Siddiqui
Renowned for their adeptness in smoothing current flow and maintaining balanced operation, 2-phase interleaved boost converters (IBC) demonstrate remarkable efficiency, especially when confronted with demanding loads. This makes them a preferred choice for high-power applications such as renewable energy systems, high-power supplies, and electric vehicle power trains. In contrast, standard boost converters are typically favored in low-power, low-demand scenarios. The control of a 2-phase IBC involves running two boost converters in parallel but with a phase shift to reduce ripple currents, improve efficiency, and increase power handling capabilities. To ensure stability and optimal performance, the control strategies for these converters focus on achieving balanced operation between the phases. Hence, the control of 2-phase IBC presents a significant challenge due to their non-minimum phase behavior. The core focus of this article is the implementation of a composite model predictive control (MPC) technique to regulate a 2-phase interleaved boost converter. It introduces a novel approach, model predictive sliding mode control (MPSMC), which leverages the strengths of both MPC and sliding mode control (SMC). The benefits of this hybrid method, termed MPSMC, are thoroughly developed and simulated using MATLAB/Simulink. The results, as discussed in the respective section, provide an in-depth understanding of its effectiveness in practical applications.
Volume: 36
Issue: 3
Page: 1421-1429
Publish at: 2024-12-01

Insights into pour point depressants: a brief review of their impact on the behavior of waxy crude oil

10.11591/ijaas.v13.i4.pp920-935
Gaurav Himanta Khaklari , Prasenjit Talukdar
A persistent challenge in the petroleum industry involves paraffin wax deposition from crude oils during low-temperature conditions, complicating pipeline flow assurance due to the intricate rheological behaviors exhibited by waxy crude oil gels. These behaviors include viscoelasticity, yield stress, and thixotropy, contributing to issues like flow reduction and pipeline obstruction, adversely affecting overall pipeline performance. Mitigating this problem requires a combination of mechanical and chemical processes, with pour point depressants (PPDs) emerging as an effective chemical solution. PPDs operate by interacting with wax crystals in the oil, disrupting their formation into a continuous network and preventing flow blockage at lower temperatures. The performance of PPDs depends on factors such as base oil type, PPD concentration, and application temperature. Recent advancements in PPDs focus on developing new polymers with enhanced performance and reduced environmental impact, including those derived from renewable resources, biodegradable PPDs, nano-structured PPDs, or hybrid PPDs. Polymeric additives, such as crystalline-amorphous copolymers, ethylene-vinyl acetate copolymers, comb polymers, and nanohybrids, are employed to modify wax crystallization behavior. Understanding the molecular structure of these additives, fluid composition, and pipeline conditions is crucial for optimizing formulations tailored to specific petroleum fluid compositions and transport conditions, ensuring effective wax deposition mitigation.
Volume: 13
Issue: 4
Page: 920-935
Publish at: 2024-12-01

Electrical circuit approaches to modeling brain chaos: insights into neural dynamics

10.11591/ijeecs.v36.i3.pp1503-1511
Kaouther Selmi , Kods Bachta , Kais Bouallegue
This paper investigates the simulation of brain chaos dynamics using a combination of the Chua circuit and diode tunnel mechanisms, aiming to examine chaotic behavior in brain networks. Leveraging the inherent chaotic properties of the Chua circuit, Fitzhugh-Nagumo (FHN) function and the nonlinear characteristics of diode tunneling, our model offers a platform to mimic the intricate synaptic interactions observed in the brain. By subjecting the model to various stimuli and perturbations, we analyze the emergence and evolution of chaotic patterns, shedding light on the underlying mechanisms of cerebral chaos. Through numerical simulations and experimental validation, we demonstrate the effectiveness of our approach in replicating key features of brain chaos and highlight its potential implications for understanding neurological disorders and cognitive processes. This research contributes to the broader effort of leveraging computational models to explore the complex dynamics of the brain and their implications for neuroscience and microengineering.
Volume: 36
Issue: 3
Page: 1503-1511
Publish at: 2024-12-01

The LLCL filter-based synchronverter with adaptive fuzzy logic controller

10.11591/ijpeds.v15.i4.pp2115-2127
Tan Thien Tran , Quoc Dung Phan , Bao Anh Nguyen , Weng Kean Yew
Global warming has emerged as a significant issue, primarily due to the use of fossil fuels for power generation. Renewable energy adoption has surged. However, stability issues arise when integrating the primary source and inertia-less nature of converter-interfaced renewable energy sources (RESs) into the traditional power system, due to its intermittent and uncertain nature. The proposed solution involves controlling converter-interfaced RES to emulate the characteristics of a conventional synchronous generator (SG), commonly known as a virtual synchronous generator or synchronverter. As it fundamentally functions as a power converter, it is crucial to consider the appropriate filter design for the synchronverter. Additionally, by emulating the characteristics of SG, the values of “virtual” inertia and droop coefficients must be chosen accurately to provide a robust response under different operating scenarios of the synchronverter. Hence, this paper proposes a novel LLCL filter-based synchronverter system along with an adaptive control strategy using a fuzzy logic controller (FLC). The system is simulated under different operating scenarios in MATLAB/Simulink. The proposed system demonstrated improved frequency response and reduced total harmonic distortion compared to the conventional synchronverter model for all the different operating scenarios, thus enhancing grid stability and power quality.
Volume: 15
Issue: 4
Page: 2115-2127
Publish at: 2024-12-01

A neural machine translation system for Kreol Repiblik Moris and English

10.11591/ijai.v13.i4.pp4976-4987
Sameerchand Pudaruth , Sheeba Armoogum , Nirmal Kumar Betchoo , Aneerav Sukhoo , Vandanah Gooria , Abdallah Peerally , Mohammad Zafar Khodabocus
Although Google Translate is a widely used machine translation service that supports 133 languages, it does not incorporate support for the Kreol Repiblik Moris (KRM) language. Addressing this limitation, the current research focuses on enhancing the accuracy and fluency of machine translation between KRM and English through natural language processing and deep neural machine translation techniques. In this study, a machine translation system using a transformer model trained with a dataset of 50,000 parallel corpora has been developed. The model was evaluated using manual translations and the bilingual evaluation understudy (BLEU) score. A score of 31.46 for translating from KRM to English and 28.15 for translating from English to KRM was achieved. To our knowledge, these are the highest BLEU scores for translation between these two languages. This is due to utilising the largest dataset and extensive atomic words from the KRM dictionary. This successful interdisciplinary funded project led to the setting up of a free online translation service and a smartphone app for Mauritian citizens and tourists.
Volume: 13
Issue: 4
Page: 4976-4987
Publish at: 2024-12-01

Risk analysis of adolescent sexual activity in college Surabaya, Indonesia: cross-sectional study

10.11591/ijphs.v13i4.24302
Eny Qurniyawati , Zida Husnina , Jayanti Dian Eka Sari , Asma Azzahra , Izzah Nur Shabrina , Prima Kartika , Nayla Mohamed Gomaa Nasr
Adolescence is characterized by active sexual hormones that can lead to sexual urges with the opposite sex. These urges, in turn, may trigger risky sexual behavior. College students, in a transitional period from adolescence to early adulthood, exhibit diversity in addressing sexual activity. This research aimed to analyze the risks associated with adolescent sexual activity in a college in Surabaya, Indonesia. The study utilizes a cross-sectional design with accidental sampling of active students aged 18-24 years. The total number of participants in this study was 221 people who filled out a questionnaire in the form of a Google Form, distributed through social media. The results show that, among all the factors identified, dating status has a significant relationship with PR 11.688 (95%CI 5.048-27.061) in engaging in risky sexual activities among adolescents. Reproductive health education at the university level is needed to reduce risky sexual activities among students.
Volume: 13
Issue: 4
Page: 1794-1802
Publish at: 2024-12-01

A comprehensive evaluation of machine learning algorithms for precise energy consumption forecasting in smart homes

10.11591/ijpeds.v15.i4.pp2138-2144
Lakshmana Phaneendra Maguluri , M. Shankar , R. Aruna , D. Chitra Devi , M. J. Suganya
Energy is one of the most critical and costly resources, playing a vital role in our daily lives. As technology advances, the demand for energy also increases. This work proposes a model for predicting energy consumption in smart homes, consisting of data preprocessing, performance evaluation, and application. Once the data is processed, it is fed into the prediction module, where various machine-learning algorithms are applied to forecast energy consumption. As smart home environments grow in complexity, selecting the most effective machine learning algorithm becomes increasingly crucial. The persistent challenge lies in manually discerning the best-performing algorithm, given their potential variance in efficacy across diverse use cases or datasets. In the dynamic landscape of energy conservation and cost-effective power generation, precise forecasting of energy consumption is essential, playing a pivotal role in advancing energy sustainability and bolstering economic stability. This introduction explores the intricate terrain of predicting energy utilization within smart homes, a domain that has seen increased interest due to the integration of machine learning algorithms. The primary focus of this exploration is the rigorous evaluation of these algorithms, using key performance metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared.
Volume: 15
Issue: 4
Page: 2138-2144
Publish at: 2024-12-01

Fuzzy logic assessment of X-ray tube risks in robotic C-arm angiography: a failure mode and effect analysis study

10.11591/ijra.v13i4.pp506-514
Ade Firdaus , Andi Adriansyah , Nanda Ferdana , Rahmalisa Suhartina , Rino Ferdian Surakusumah , Jakfat Haekal , Zulhamidi Zulhamidi , Abu Ubaidillah Shamsudin
This research examines the integration of robotic C-arm technology in angiography, a critical tool for treating cardiac conditions. The robotic C-arm, which includes an X-ray tube, is essential for scanning patients during procedures. The study also investigates the associated risks, specifically in Indonesian hospitals with cardiac facilities. Angiography is used to diagnose and treat heart disease by visualizing blood vessels and facilitating catheterization procedures. However, its mobility poses hazards and can impact the process. To address potential risks, failure mode and effect analysis (FMEA) is utilized. Traditionally, risk assessment using risk priority numbers (RPN) is conducted, but these may not accurately reflect failures due to complex evaluating processes. To overcome this limitation, fuzzy logic is employed, enhancing risk assessment accuracy. Through this approach, twenty-seven failure modes are identified across two brands, with ten major ones prioritized using fuzzy logic. These findings facilitate the development of preventive measures to mitigate future failures and enhance patient safety during angiography in hospitals. In conclusion, the study underscores the importance of robust risk management in medical equipment, particularly in dynamic environments. By integrating fuzzy logic into risk assessment, the study improves prioritization accuracy, enabling effective allocation of resources for preventive actions.
Volume: 13
Issue: 4
Page: 506-514
Publish at: 2024-12-01

Active balancing charging using ANFIS to reach longest lifetime for lithium ion

10.11591/ijpeds.v15.i4.pp2168-2179
Farid Dwi Murdianto , Indhana Sudiharto , Azzahra Farah Andraeni
This research aims to address challenges in efficient energy storage in electric vehicles, particularly those using lithium-ion batteries. The research focuses on voltage imbalances in battery packs with cells connected in series, which can reduce the battery pack's lifespan. We investigated active balancing charging methods using bidirectional fly-back converters in both pack-to-cell and cell-to-pack modes. Current charging methods face limitations such as prolonged balancing times, circuit complexity, and low efficiency. To overcome these challenges, this research proposes integrating an artificial intelligence approach using adaptive neuro-fuzzy inference system (ANFIS) control. This approach allows for dynamic adjustment of duty cycles, expediting the balancing process. Experimental results show that employing ANFIS control can significantly reduce balancing times. In pack-to-cell mode, balancing time decreased from 660 seconds to 600 seconds, while in cell-to-pack mode, it decreased from 660 seconds to 580 seconds. This research makes a significant contribution to the development of more efficient charging technology for lithium-ion batteries in electric vehicles. Its potential implications include improved battery lifespan and electric vehicle performance in the future.
Volume: 15
Issue: 4
Page: 2168-2179
Publish at: 2024-12-01

Image classification in cultural heritage

10.11591/ijai.v13.i4.pp4722-4735
Muath Sabha , Muhammed Saffarini , Rami Yousuf
In this paper, an automated supervised image classification technique, specifi- cally for classifying images in the cultural heritage domain, is developed. The developed technique classifies images according to a particular date, culture, people and historical age. The proposed technique consists of two stages, fea- ture extraction using the unsupervised segmentation technique, and the classi- fication stage using supervised classification techniques. Common features are extracted, and their histograms are applied to three classifiers: k-nearest neigh- bor (KNN), logistic regression (LR), and decision tree (DT). When our tech- nique was applied to a repository of images from cultural heritage, it showed reduced complexity and improved classification accuracy. DT has achieved a higher weighted average recall. This is also represented by the weighted av- erage f-measure where DT has obtained 0.81. DT has outperformed the other classifiers in terms of classifying heritage images.
Volume: 13
Issue: 4
Page: 4722-4735
Publish at: 2024-12-01

Application of inverter input rating method and standard AC voltage drop/over method on automatic transfer switch for hybrid powered e-bike charging station

10.11591/ijpeds.v15.i4.pp2480-2492
Mohammad Noor Hidayat , Azam Muzakhim Imammuddin , Abdullah Faiq Munir , Aji Nugroho
Automatic transfer switch (ATS) is useful for integrating two different energy sources can be applied to e-bike charging stations. Renewable energy sources from sunlight using 2×100 Wp photovoltaic (PV) modules as the main source in this study, as well as grid energy sources functioned as backup energy. This ATS uses inverter input rating methods and standard AC voltage drop/over, a novelty of this study. The amount of current during testing is up to 14.09 A. The value of the inverter input voltage affects the quality of the inverter output voltage, there is a stability of the inverter output voltage from the AC voltage standard ±5% of the, lowest voltage value of (Vo_inv) = 223.9 V and the highest value (Vo_inv) = 226.1 V, at the time of (Vi_inv) max = 14.15 V and (Vi_inv) min = 12.66 V. It can be concluded that the ATS works in the PV position of the supply to the e-bike load, in the PV state it can produce power of a maximum value of 135.9 W at 11:25 am and is at the lowest power of 31 W at 03:31 pm. This ATS device in the switch mode successfully implemented a duration range of 10-21 ms, during the transfer of energy.
Volume: 15
Issue: 4
Page: 2480-2492
Publish at: 2024-12-01

Analysis of single layer artificial neural network neuromorphic hardware chip

10.11591/ijra.v13i4.pp495-505
Aruna Pant , Adesh Kumar , Piyush Kuchhal
The neuromorphic architectures are hardware network systems designed with neural functions. Neural networks seen in biology serve as an inspiration for network systems. A synapse connects every node or neuron in an artificial neural network (ANN) to every other node. As in biological brains, the amplitude of the linking between nodes referred to as synaptic weights will regulate the connection. In contrast to conventional design, ANN uses many highly organized dealing pieces that work together to solve real-world issues. The design of the neuromorphic hardware chip is discussed in the paper. The target device used is a Virtex-5 Field Programmable Gate Array (FPGA) and the simulation is taken on Xilinx ModelSim software. This chip is designed for 20 neuron inputs, each of the neuron inputs is 8-bit. Each 20-neuron input is multiplied by 20 input weights and each weight is 8-bit so when these 20 input weights are multiplied by 20 neuron inputs in the multiplier it gives 16-bit output. A control logic is used in this neuromorphic hardware chip design which is used to feed multiplier output to each input of the hidden layer. The system-level outcome of the hidden layer is then given to the multiplexer which has 20 inputs and one single output. The multiplexer is used to select any of the 20 outputs of the control logic. Finally, to gain an understanding of the performance of this neuromorphic hardware chip, we have computed the hardware utilization parameters. These parameters include slices, input/output blocks (IOBs), registers used, memory, and the overall propagation delay used by the hardware chip.
Volume: 13
Issue: 4
Page: 495-505
Publish at: 2024-12-01

Implementing gamification in campus canteen using MDA framework: an overview

10.11591/ijict.v13i3.pp422-427
Florentia Novena Wibowo , Gunawan Wang
This study describes the creation of a mobile-based gamification design for an online canteen system. Long lunch queues make students feel uncomfortable ordering food in the canteen, although student comfort is very important. The increasing number of students causes long queues, resulting in significantly shorter lunch hours and causing discomfort for students. To address this issue, the campus might develop an online canteen using gamification. Gamification is a method that applies gaming knowledge to create experiences that encourage and engage people in non-game environment. Compared to traditional canteen systems, an online canteen that uses gamification can provide students with new experiences by offering attractive rewards, increasing motivation to order food and beverages online, and minimizing the perception of long lineups. Although this proposed design has not yet been tested, researchers believe it has practical applications.
Volume: 13
Issue: 3
Page: 422-427
Publish at: 2024-12-01

Efficient deep learning models for Telugu handwritten text recognition

10.11591/ijeecs.v36.i3.pp1564-1572
Buddaraju Revathi , B. N. V. Narasimha Raju , Boddu L. V. Siva Rama Krishna , Ajay Dilip Kumar Marapatla , S. Suryanarayanaraju
Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively.
Volume: 36
Issue: 3
Page: 1564-1572
Publish at: 2024-12-01
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