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31,458 Article Results

Inventory model with trade credit policy and carbon emission preservation technology

10.11591/ijeecs.v43.i3.pp824-833
Deep Kamal Sharma , Ompal Singh
This study develops an integrated inventory model that incorporates preservation technology, trade credit policy, inflation effects, and carbon emission tax considerations to optimize supply chain performance. Preservation technology is introduced to reduce product deterioration, thereby extending shelf life and improving consumption efficiency, while trade credit policies provide financial flexibility for retailers. The model reflects the growing influence of regulatory frameworks and technological advancements on inventory management, highlighting how these factors shape future commercial applications. By combining financial mechanisms with sustainability measures, the proposed framework offers a holistic approach to inventory control. The model is formulated mathematically and solved using MAPLE software, enabling precise evaluation of inventory costs under varying cycle lengths. Numerical examples validate the model’s applicability, demonstrating significant reductions in overall supply chain costs when preservation and credit policies are effectively integrated. Sensitivity analysis further explores the impact of key parameters-including deterioration rate, inflation, preservation cost, and carbon emission taxes-providing managerial insights into their role in inventory decisions. The findings confirm that integrating sustainability measures with financial and operational policies enhances cost efficiency, supports environmental responsibility, and strengthens supply chain resilience in modern business contexts.
Volume: 43
Issue: 3
Page: 824-833
Publish at: 2026-09-01

Advanced wireless electric vehicle charging system with cascaded fuzzy control and multi-stage rectification

10.11591/ijape.v15.i3.pp1253-1263
Naveena Sandhadi , Srinu Naik Ramavathu
The growing demand for electric vehicles (EVs) has intensified the need for high-efficiency, reliable, and user-friendly wireless charging systems. Conventional wired chargers and existing wireless solutions often suffer from high losses, poor power quality, and limited control accuracy under dynamic operating conditions. To address these challenges, this paper proposes an advanced wireless charging architecture integrating a three-phase Vienna rectifier, high-frequency inverter, and isolation transformer. A three-phase frequency inverter and a three-stage synchronous rectifier further enhance conversion efficiency. Precise voltage and current regulation is achieved using a cascaded fuzzy controller (CFC) combined with a proportional-integral (PI) controller. MATLAB simulations demonstrate unity power factor, improved efficiency, and stable performance under varying load conditions. The results confirm that the proposed architecture offers a scalable, robust, and energy-efficient solution capable of advancing EV charging infrastructure and supporting sustainable transportation.
Volume: 15
Issue: 3
Page: 1253-1263
Publish at: 2026-09-01

Analysis a mixtures of bentonite, palm kernel shell charcoal and magnesium sulfate (MgSO4) for reducing grounding resistance using rod-type electrodes at varying soil depths

10.11591/ijape.v15.i3.pp1386-1398
Ferry Rahmat Astianta Bukit , Naemah Mubarakah , Adrian Sinaga
A grounding system is an essential electrical safety mechanism designed to protect humans and equipment from disturbances such as lightning-induced surge currents or short circuits. It operates by channeling excess current into the ground through grounding electrodes, thereby reducing the risk of damage and hazards. High grounding resistance can compromise the dissipation of fault currents and overvoltages, leading to safety risks. This study focuses on reducing grounding resistance through chemical soil treatment using bentonite combined with a mixture of magnesium sulfate (MgSO₄) and palm shell charcoal. The optimal composition consists of 10% bentonite, 10% native soil, and 80% of the material mixture. Experiments were conducted at electrode depths of 30 cm, 60 cm, 90 cm, and 110 cm. Results showed that the initial resistance of 312.5 Ω at 30 cm depth decreased to 86.1 Ω, and at 110 cm depth, resistance decreased from 186.5 Ω to 53.0 Ω. The average reduction reached 77.4%, indicating that this material combination is highly effective in lowering grounding resistance and improving system performance.
Volume: 15
Issue: 3
Page: 1386-1398
Publish at: 2026-09-01

Improved microgrid energy coordination via hybrid PSO and bidirectional EV integration: performance comparison against genetic algorithm

10.11591/ijape.v15.i3.pp1475-1483
Bilal Amghar , Toufik Azib , Khelil Sidi Brahim
This paper presents a comparative study between hybrid particle swarm optimization (PSO) and genetic algorithm (GA) for energy management in residential microgrids equipped with photovoltaic generation, stationary battery storage, and bidirectional electric vehicles (V2G). The system comprises 40 apartments, 1000 m² solar panels, 1 MWh battery storage, and 15 electric vehicles with V2G capability. A multi-objective optimization framework minimizes daily operational costs while satisfying mobility requirements, state-of-charge constraints, and battery aging considerations. Simulation results demonstrate that hybrid PSO significantly outperforms GA, achieving a net daily profit of 279 C (compared to 100 C cost for GA) through strategic energy arbitrage and massive grid sales (2232 kWh/day vs 3.7 kWh/day for GA). The PSO-based approach achieves 28% energy autonomy while generating substantial revenue from feed-in tariffs. The methodology provides a scalable framework for real-world V2G-integrated microgrids, with ongoing experimental validation at the ESTACA V2G testbed.
Volume: 15
Issue: 3
Page: 1475-1483
Publish at: 2026-09-01

Multi-agent intelligence with privacy awareness for robust marine robotic electric drives

10.11591/ijpeds.v17.i3.pp1702-1713
Vishnu Kumar Mishra , Megha Mishra , T. Ram Kumar , Yenna Geetha Reddy , Sri Lavanya Sajja , Gundla Rajesh , Bandla Srinivasa Rao
This paper introduces a privacy-preserving multi-agent intelligent model tailored towards improving the movement control and energy management of robotic marine electric motors. In particular, the research incorporates the use of a fractional-order neural network algorithm (FONNA) combined with federated learning, allowing for decentralization without compromising raw data security. The modeling process entails the application of a seven-phase voltage source inverter (VSI) to offer high-performance motor behavior and exceptional fault resilience. Some of the significant discoveries include the delivery of 96.96% peak efficiency at 55 kHz and decreasing dependency on high-bandwidth communication by 75% via gradient updates. Energy resilience remains an essential prerequisite for collaborative marine robotics in harsh offshore conditions where the electricity supply can be subjected to disruption. However, even with all these developments, robotic fleets for maritime purposes are faced with some major constraints when working together. For instance, the centralized approach is easily affected by system failures since all operations will be affected if one fails. The marine area poses harsh conditions in which communication bandwidth and electrical power may be difficult to provide.
Volume: 17
Issue: 3
Page: 1702-1713
Publish at: 2026-09-01

Design and analysis of low-k dielectric TSV liners for noise mitigation in high-frequency 3D ICs

10.11591/ijict.v15i3.pp1188-1196
Pathakunta Guru Prathap Reddy , Sravan Abhilash Kothapalli
Moore’s Law has driven the development of very large-scale integration (VLSI) technology, allowing continuous transistor scaling to increase speed, density, and performance. However, as two-dimensional (2D) integrated circuits (ICs) near their physical and performance boundaries, and 2.5D ICs still face interconnect delay and power issues, three-dimensional (3D) integration has become a practical solution. In 3D ICs, multiple active layers are vertically stacked and connected via through-silicon vias (TSVs), providing short, high-bandwidth interconnects between layers. Electrical TSVs are essential for signal transmission, but also cause noise coupling between adjacent TSVs, where an aggressive TSV can induce interference in a nearby TSV. This coupling can impair signal integrity, increasing delay and power consumption. To mitigate this, low-dielectric-constant (low-k) materials are used to reduce capacitive coupling. In this study, materials such as benzocyclobutene (BCB), Perylene-N, and Teflon AF 1600 are compared with conventional SiO₂. Generally, TSVs are two structures — single-liner and stacked-liner — which are analysed at 10 GHz and 1 THz frequencies. At 10 GHz, the single-liner structure incorporating SiO₂ exhibits a noise reduction of about 6.56 dB, whereas the stacked-liner configuration using Teflon AF 1600 provides a noticeably greater reduction of 8.40 dB. As the operating frequency increases to 1 THz, the advantage of the low-k dielectric becomes more evident, yielding 9.63 dB noise reduction for the single-liner and 12.04 dB for the stacked-liner structure. These results indicate that low-k materials effectively suppress capacitive coupling and mitigate high-frequency interference in 3D ICs. The stacked-liner design contributes additional isolation by creating a secondary dielectric barrier, which further minimizes electric field interaction between neighboring interconnects. Thus, the integration of low-k dielectrics with optimized liner architectures significantly enhances signal integrity and overall electromagnetic performance in advanced high-frequency 3D IC systems.
Volume: 15
Issue: 3
Page: 1188-1196
Publish at: 2026-09-01

Insight invest: sentiment-aware stock prediction using LSTM and conversational interface

10.11591/ijict.v15i3.pp1115-1122
Ankit Pande , Aakhyan Jeyush , Abhishek K. Lakhote , Saket A. Rathi , Manoj B. Chandak
The volatile nature of financial markets requires sophisticated tools that integrate advanced analytics with accessible interfaces to facilitate informed investment decisions. This research introduces Insight Invest, an intelligent investment assistant that combines sentiment analysis with time-series forecasting to deliver comprehensive stock market insights. The platform introduces the emotional quotient (EQ), a novel metric derived from the sentiment analysis of financial news, to quantify market sentiment and align it with historical stock price data. Leveraging long short-term memory (LSTM) models, the system provides precise predictions of future stock trends. Automated data collection and processing are achieved through a Flask-based backend, while an OpenAI-powered chatbot delivers intuitive interpretations of predictions and EQ values. The user-centric design, implemented using Next.js, ensures a seamless and responsive experience. By integrating state-of-the-art machine learning techniques with intuitive interfaces, Insight Invest bridges the gap between complex predictive analytics and practical usability, offering a robust framework for informed investment strategies.
Volume: 15
Issue: 3
Page: 1115-1122
Publish at: 2026-09-01

Metaverse based immersive learning prototype for satellite communication using silvercoms model

10.11591/ijict.v15i3.pp1408-1418
Komputerio Akbar , Meyliana Meyliana , Harco Leslie Hendric Spits Warnars , Ilvico Sonata
Advancements in immersive technologies and the metaverse are transforming engineering education. Satellite communication, as a complex and abstract domain, requires innovative approaches to enhance conceptual understanding and learner engagement beyond traditional methods. This study proposes the Silvercoms model, an immersive learning framework tailored for satellite communication systems education, integrating metaverse technology with pedagogical design. The model combines 3D interactive simulations, collaborative virtual environments, and systems-level content delivery, structured using the 6E instructional model and supported by the motivated strategies for learning questionnaire (MSLQ) to address both cognitive and motivational aspects of learning. The system is developed using a systems engineering approach and implemented with Unity3D in a virtual reality (VR) based metaverse environment. The prototype includes modules such as satellite orbit simulation, satellite history, and interactive satellite systems, enabling experiential and concept-driven learning. This study contributes by integrating immersive technology with structured pedagogical frameworks, offering a novel approach to improving learning effectiveness in satellite communication systems education.
Volume: 15
Issue: 3
Page: 1408-1418
Publish at: 2026-09-01

Advanced encryption standard with asymmetric key exchange for text encryption

10.11591/ijict.v15i3.pp1263-1271
Ravindra K Reddy , Vijayalakshmi P
When creating communication systems, securing data is crucial and improved randomization in creating secret keys contributes to more secure systems. Unfortunately, the symmetric ciphers used for data encryption, such as the advanced encryption standard (AES), may be subject to attacks that exploit timing measurements to deduce the secret key used for encryption, resulting in a significant lack of research on the security of hybrid implementations (usually defined as incorporating AES and asymmetric ciphers) for AES encryption. We introduce a new hybrid encryption method that combines the AES encryption standard with elliptic curve cryptography (ECC). In this hybrid form of encryption, ECC is utilized to facilitate secure encryption and transmission of the AES key and cycle through 16 rounds of AES to encrypt the majority of the data. We provide a performance comparison between the new algorithm and the AES-128 encryption standard. Our findings indicate that the new hybrid encryption method produced an average encryption time of 0.0002 seconds for 10MB files, significantly faster than the AES-128 encryption standard, which takes an average of 0.0016 seconds. The new hybrid method exhibits significant resistance to cryptanalytical attempts, experiencing an average avalanche effect of 49.84%, while maintaining the AES nonlinearity value at 112. As such, we conclusively state that the new hybrid encryption method utilizing ECC and AES will provide effective security for user data against timing side-channel attacks while remaining an efficient method for performing encryption.
Volume: 15
Issue: 3
Page: 1263-1271
Publish at: 2026-09-01

Wavelet-based spectrum sensing with improved thresholding for enhanced detection in cognitive radio networks

10.11591/ijict.v15i3.pp1123-1134
Nur Hanis Abdul Rani , Mas Haslinda Mohamad , Nurusolihah Zamri , Nor Khairiah Ibrahim
Cognitive radio (CR) technology is an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum. Spectrum sensing is the most important component in CR due to its ability to sense and recognize parameters related to the radio channel characteristics. However, there are some spectrums that are not used known as spectrum holes. It is challenging to accurately identify these spectrum holes, especially when employing traditional energy detection techniques, which suffer from incorrect threshold selection at low signal-to noise ratio (SNR) levels. This work suggests a wavelet-based spectrum sensing technique in conjunction with an enhanced thresholding method to improve detection accuracy and decrease noise to overcome this constraint. MATLAB simulations are used for evaluating three threshold functions: hard, soft, and improved. The results indicate that the improved threshold achieves superior denoising performance and a higher detection probability compared to the traditional energy detection method. In this study, the energy detection technique was also implemented for comparison with the wavelet-based approach. The findings reveal that wavelet-based sensing consistently provides a higher detection probability (𝑃𝐷𝐸𝑇), demonstrating its effectiveness and reliability for cognitive radio (CR) application.
Volume: 15
Issue: 3
Page: 1123-1134
Publish at: 2026-09-01

A recent hybrid of IoT with adaptive extended Kalman filter fuzzy logic for children’s health dietary

10.11591/ijict.v15i3.pp1217-1225
Noorrezam Yusop , Massila Kamalrudin , Mohd Nazrien Zaraini , Siti Fairuz Nurr Sardikan
The increasing prevalence of childhood obesity highlights the critical need for intelligent dietary monitoring systems that are tailored to individual nutritional requirements. This work describes the creation and testing of an internet of things (IoT) hybrid using an adaptive extended Kalman filter and fuzzy logic (AEKFFL-IoT) model aimed at providing personalised food calorie prediction for children. The system uses IoT devices to collect real-time sensor data, such as height, weight, and BMI, and then employs extended Kalman filter (EKF) algorithms to denoise signals and anticipate trends. Fuzzy logic inference is then utilised to adaptively calculate caloric requirements based on biometric data. Experimental results reveal that the proposed AEKFFL model has a training root mean square error (RMSE) of 21.43 kcal and a testing RMSE of 22.35 kcal, exceeding existing rule-based, wearable, and ANN-driven models in terms of accuracy and generalisation. Furthermore, the system achieves high classification accuracy (94.5%) for BMI categorisation and fuzzy rule application. Comparative examination confirms the model’s adaptability, real-time integration, and mobile deployment capability. This study presents a scalable and intelligent approach for child-centered dietary monitoring, paving the path for personalised digital health interventions.
Volume: 15
Issue: 3
Page: 1217-1225
Publish at: 2026-09-01

Enhanced anomaly detection in IoT networks via feature fusion and learning-based echo state networks

10.11591/ijict.v15i3.pp1154-1166
P. Palpandi , B. Sakthivel , M. Ponnrajakumari , M. Indirani , S. Govindaraju , S. Deivasigamani
The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
Volume: 15
Issue: 3
Page: 1154-1166
Publish at: 2026-09-01

Prognosis of vector borne dengue disease outbreak in urban areas using multivariate analysis

10.11591/ijict.v15i3.pp1066-1077
Pratik S. Machchar , Purvi N. Ramanuj , Rajan Patel , Jitendra Bhatia , Kuntesh Jani
Vector borne disease like dengue continues to pose a significant climate-sensitive public health challenge in tropical regions such as Brazil, Peru, and India. This study examines the feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan (SJ), Puerto Rico and Iquitos (IQ), Peru. Dengue incidence was analyzed alongside meteorological, environmental, and vegetation-based variables to capture key climatic influences. Several machine learning and deep learning approaches were evaluated, including LightGBM. Model performance was assessed using root mean square error (RMSE) and mean absolute error (MAE). The results show that LightGBM achieved the low est RMSE/MAE, indicating strong short-term predictive accuracy and excellent interpretability. Feature importance analysis and principal component analysis (PCA) identified precipitation, dew point temperature, and humidity as the most influential predictors of dengue incidence. The study demonstrates that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases. While this research focuses on dengue, the methodology is adaptable to other vector-bone datasets and diseases, offering a flexible tool for public health authorities to predict and mitigate outbreaks in diverse urban contexts.
Volume: 15
Issue: 3
Page: 1066-1077
Publish at: 2026-09-01

Multi-criteria optimization of emergency unit allocation using COPRAS and SMART: a case study in Palembang

10.11591/ijict.v15i3.pp1419-1430
Evi Yuliza , Fitri Maya Puspita , Indrawati Indrawati , Sisca Octarina , Frisca Frasilia
Increasing living standards and instant eating patterns have improved people's demands for quality health services. Hospitals as health service facilities are actually real-time networks that expected to be able to provide effective and efficient services. This research uses the complex proportional assessment (COPRAS) and simple multi-attribute rating technique (SMART) methods to determine the hospital with the most optimal emergency unit (EU) services in each subdistrict based on predetermined criteria. The research results show that the COPRAS method is produces performance index values ranging from 0.0195 to 0.1317, while the SMART method yields scores between 0.054 and 0.122, both demonstrating consistent ranking outcomes. The three hospitals, with the most optimal EU performance are Dr. Mohammad Hoesin, RSU Pertamina, and RSJ Ernaldi Bahar, with Dr. Mohammad Hoesin achieving the highest utility value (0.1317). The novelty of this study lies in the integration of real-time spatial and operational data from Google Maps and RS Online into a hybrid set covering problem (SCP) framework, combining the strengths of COPRAS and SMART.
Volume: 15
Issue: 3
Page: 1419-1430
Publish at: 2026-09-01

Conversational AI in museums: a systematic literature review using the people–process–technology framework

10.11591/ijict.v15i3.pp955-966
Diana Utomo , Siti Elda Hiererra
The adoption of conversational artificial intelligence (AI) in museums has opened a new opportunity to create a richer visiting experience to tell stories for the preservation of culture. This paper contributes a systematic literature review (SLR) of 33 peer-reviewed papers covering the period from 2020 to mid-2025, by applying the people–process–technology (PPT) model to examine the social technological aspects of AI implementation. This combination is novel in a museum context, as previous research has largely treated these separately. Findings indicate a pronounced shift from text based chatbots (21% or 7 of 33 papers) to more immersive and interactive platforms (30% or 10 of 33 papers), reflecting the transition from the pandemic (2020 – 2023) to the post-pandemic period (2024 – 2025). Besides the evolution of these technologies, the technology component highlights the importance of data governance, digital preparedness, and value alignment at and across different levels. The people component includes the relevance of hedonic and utilitarian values. Meanwhile, the process component emphasizes both strategic and technical aspects of AI design and implementation, such as knowledge structuring, media selection, and narrative representation. In line with this agenda, this study addresses the trends in literature and provides a path for sustainable appropriation by museums.
Volume: 15
Issue: 3
Page: 955-966
Publish at: 2026-09-01
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