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29,922 Article Results

Parametric optimization of microchannel heat exchanger using socio-inspired algorithms

10.11591/ijai.v14.i6.pp5303-5310
Vikas Gulia , Aniket Nargundkar
Miniaturized products and systems have emerged as game-changing innovations with huge potential in the modern period with increasing emphasis on sustainable development and green energy. Automotive, astronomical, electronics, and medical research are just a few of the industries where micro electro mechanical systems (MEMS) have found use. In addition to that, microchannel heat exchangers (MCHX) have been created in response to the growing demand for effective cooling solutions for these small systems. Optimization of these MCHX is important for improving the overall system efficiency. In this work, two popular socio inspired evolutionary algorithms viz. teaching learning-based optimization (TLBO) and cohort intelligence (CI) are applied for optimizing three objectives such as power density, compactness factor, and heat transfer with pressure drop (HTPD) for air-water MCHX. The results obtained are significantly improved when compared with genetic algorithm (GA). Moreover, both the techniques are observed to be robust. This study investigates the use of socio-inspired artificial intelligence (AI) algorithms to support the design and optimization of heat exchangers, highlighting their potential to address complex engineering challenges more efficiently.
Volume: 14
Issue: 6
Page: 5303-5310
Publish at: 2025-12-01

Bioecological characteristics of modern soil cover in subtropic regions of Azerbaijan

10.11591/ijaas.v14.i4.pp1200-1207
Farida Verdiyeva Bahram , Turkan Hasanova Allahverdi , Mahsati Ismayilova Eyvaz , Elnur Huseynov Yusif , Telli Jabiyeva Elshad , Gunel Asgarova Farhad
The purpose of this study is to introduce innovation in the field of agriculture in Azerbaijan by determining the abundance of various ecotrophic groups of microorganisms (involved in the formation and mineralization of humic substances) in natural and cultivated gray-brown soils. Studying the microbiological indicators of humic substance transformation in virgin soils and determining the direction of these processes under the influence of anthropogenic factors in agrocenoses soils is considered relevant for the development of the agricultural sector in the Lankaran region. It was found that perennial woody vegetation increased the abundance of pedotrophic microorganisms by 17-21% and humate decomposers by 12-14% compared to completely natural soil. The correlation coefficient between the abundance of humate decomposers and the pedotrophic index was r=-0.685±0.09. Plowing natural gray-brown soils reduces the total humus content and the abundance of micromycetes, which form the peripheral portion of humic substances.
Volume: 14
Issue: 4
Page: 1200-1207
Publish at: 2025-12-01

Multi-dimensional brand experiences in co-branded products across generations

10.11591/ijaas.v14.i4.pp1018-1027
Yana Erlyana , Lim Jing Yi
As consumer expectations evolve, brands are tasked with creating multifaceted experiences that resonate with different generations. This study examines the influence of sensory, affective, behavioral, and cognitive brand experiences on consumer perceptions of co-branded products, with a focus on two key cohorts: Generation Y (Gen Y) and Generation Z (Gen Z). A mixed-methods approach, integrating quantitative surveys and qualitative focus groups, was employed to gain deeper insights into generational differences in brand engagement. The findings reveal that Gen Y consumers prioritize emotional and behavioral experiences, seeking meaningful interactions and emotional connections that align with their values and life stages. In contrast, Gen Z consumers are more interested in sensory novelty and cognitive engagement, favoring brands that emphasize originality, digital interactions, and distinctive experiences. Both generations showed strong reactions to behavioral factors, particularly direct product interactions. These insights highlight the importance of tailoring brand experience strategies to the unique preferences of each generation. By embedding sensory, emotional, and cognitive elements into brand experiences, companies can create deeper emotional connections with consumers, enhance brand value, and build long-term loyalty. The results offer actionable strategies for brand managers seeking differentiation and sustainable success in today’s competitive market environment.
Volume: 14
Issue: 4
Page: 1018-1027
Publish at: 2025-12-01

New approach of the neighborhood structure of fuzzy points

10.11591/ijaas.v14.i4.pp1083-1088
Amer Himza Almyaly , Jwngsar Moshahary
This paper provides a comparative analysis of the fuzzy Q-neighborhood and the fuzzy neighborhood system of a fuzzy point. Specifically, we investigate the relationship between the elements of these systems when both are defined at the same fuzzy point. We address questions such as: how are these elements interconnected, and which system contains the other? Furthermore, we give the dual of the fuzzy Q-neighborhood system, which is named the fuzzy DQ-neighborhood system, and prove that these two systems are not equivalent. Finally, we examine the properties of these systems to determine whether they satisfy the conditions of fuzzy topology, Supra topology, or filter theory.
Volume: 14
Issue: 4
Page: 1083-1088
Publish at: 2025-12-01

A survey on convolutional neural network hardware acceleration through approximate computing multiple and accumulates unit

10.11591/ijra.v14i4.pp366-375
Suvitha Pathiyadan Sudhakaran , Aathmanesan Thangakalai
Convolutional neural networks (CNNs) are applied to a different range of real-world complex tasks to provide effective solutions with high accuracy. Based on the application's complexity, CNN demands a lot of processing units and memory spaces for its effective implementation. Bringing this computational task to hardware for processing the data to enhance the acceleration helps in achieving real-time performance improvement. Recent studies focused on approximation methodology to overcome this problem. This proposed survey analyzes various recent methods involved in implementing approximating computing-based processing elements and their usage in CNNs. Primarily, the survey focuses on multiple and accumulates (MAC) unit and their various approximation methods, which acts as a fundamental block as a processing element in the CNN layers. Secondly, it focuses on various CNN hardware acceleration architectures and their layers designed using different methods and their wide range of applications. Some of the recent design methods applied to various ranges of applications are also analyzed in the proposed survey. This detailed analysis gives an outlook on effective approximation blocks and the CNN architecture to be effectively used in various designs, with a scope of area in which future improvement can be made.
Volume: 14
Issue: 4
Page: 366-375
Publish at: 2025-12-01

Thermally stable sol-gel yttrium aluminum garnet cerium phosphors for white light-emitting diodes

10.11591/ijaas.v14.i4.pp1367-1374
Phan Xuan Le , Nguyen Thi Phuong Loan , Nguyen Doan Quoc Anh , Hsiao-Yi Lee
This study aims to develop structurally controlled TiO2-based materials that serve a dual purpose as high-performance photocatalysts and optical scattering agents for white light-emitting diodes (LEDs). Hollow spherical TiO2, TiO2/Ag, and TiO2/Au particles were synthesized via a one-step spray thermolysis process using aqueous titanium citrate and titanium oxalate precursors. The method enables precise control of morphology and crystalline phase composition, producing hollow microspheres with tunable anatase–rutile ratios (10–100%) and crystallite sizes ranging from 12 to 120 nm. Photocatalytic performance, evaluated through the ultraviolet (UV) driven oxidation of methylene blue, showed that as-prepared TiO2 exhibited comparable activity to Degussa P25, while metal doping accelerated the anatase-to-rutile transition with minimal plasmonic enhancement under UV light. For LED applications, incorporating hollow TiO2 particles into YAG:Ce phosphor films improved luminous intensity, reaching a peak of ∼71 lm at 1 wt.% TiO2, and enhanced color uniformity, achieving a D-CCT as low as ∼60 K at 5 wt.%. These results confirm that spray thermolysis provides a scalable route to tailor morphology and phase composition, enabling multifunctional TiO2 materials optimized for both environmental photocatalysis and high-quality LED lighting.
Volume: 14
Issue: 4
Page: 1367-1374
Publish at: 2025-12-01

Antimicrobial activity of hard candy with basil (Ocimum sanctum L.) essential oil addition

10.11591/ijaas.v14.i4.pp1061-1071
Maria Belqis , Giyarto Giyarto , Moch Yusuf Irvanto , Fitri Setyoningrum , Siti Susanti
The basil plant belongs to the Lamiaceae family and contains various active compounds, including phenols, saponins, alkaloids, flavonoids, tannins, and essential oils. These compounds have antimicrobial activity against Streptococcus mutans and Candida albicans, two types of bacteria that can cause bad breath. The addition of basil essential oil to hard candy has the potential to reduce bad breath. This study aimed to determine the concentration effect of basil essential oil on hard candy in inhibiting the growth of Streptococcus mutans and Candida albicans and its acceptance by the panelists. This research was conducted with five treatments with variations in the concentration of basil essential oil, which were 0, 0.25, 0.5, 0.75, and 1%. The results showed that the higher basil essential oil concentration in hard candy inhibited the growth of Streptococcus mutans and Candida albicans. The best treatment was at 0.75% basil essential oil, with sensory panelist acceptance for color 69%, aroma 57%, taste 43%, and overall 58%. Several compounds in basil essential oil, including linalool, eugenol, caryophyllene, and trans-α-bergamotene, are thought to contribute to the ability of this candy to inhibit microbial growth.
Volume: 14
Issue: 4
Page: 1061-1071
Publish at: 2025-12-01

Unveiling anomalies in industrial control systems: a kernel SHAP-based approach with temporal convolution autoencoder

10.11591/ijaas.v14.i4.pp1420-1432
Sangeeta Oswal , Subhash Shinde , Vijayalaksmi Murli
Industrial control systems (ICS) are often the target of cyber-attacks, leading to undesirable consequences. ICSs operate without human supervision, making them vulnerable to adversaries. In recent years, numerous deep learning-based solutions have demonstrated their efficiency in detecting anomalies in ICSs. However, there is a lack of ability to pinpoint the sensors and actuators that contributed to the anomaly. In this research work, we use kernel Shapley additive explanations (SHAP) to explain anomalies detected by a temporal convolution autoencoder (TCAE). The proposed TCAE model handles the long-term dependency effectively and is computationally effective on a large dataset. A comprehensive explanation is provided, focusing on the feature that contributed to the anomaly for each identified attack. The SHAP values are extracted for each identified attack and visually depict the feature that contributed to the anomaly for each attack, helping the expert to handle the attack and build user trust.
Volume: 14
Issue: 4
Page: 1420-1432
Publish at: 2025-12-01

Optimizing smart grids with blockchain-driven automation and demand response

10.11591/ijaas.v14.i4.pp985-998
B. Jyothi , Bhavana Pabbuleti , Ravi Ponnala , Kambhampati Venkata Govardhan Rao , S. Sai Srilakshmi , Putta Dhanush Narasimha , Mareboyina Karthik Yadav , Malligunta Kiran Kumar , Ch. Rami Reddy
To increase resilience, efficiency, and engagement in the network, it shall develop and test its smart grid system integrating blockchain-based authentication and automated demand response management. Simulations are made on the dynamic behavior of the grid in energy generation, consumption, and management through demand responses through MATLAB/Simulink assessment of performance and stability. Ethereum is used in implementing and managing smart contracts that automate and secure events of demand response and consumer interactions for transparency in transactions. It uses Python with Pandas to process, analyze, and visualize simulation data that gives insight into the effectiveness of demand response strategies; PostgreSQL supports the structured storage and querying of data with comprehensive data management. Proper integration of such tools can result in the proper robust simulation of the smart grid system that is highly reliable, efficient usage of energy, and can empower consumers through secure, efficient demand response mechanisms. These immediate issues about managing the grid can thus solve the way toward the future development of such smart grid technologies and their possible integration with the blockchain.
Volume: 14
Issue: 4
Page: 985-998
Publish at: 2025-12-01

Advancements in electric vehicle safety and charging infrastructure

10.11591/ijaas.v14.i4.pp1332-1339
Debani Prasad Mishra , Rudranarayan Senapati , Nisha Kedia , Sanchita Sahay , Raj Alpha Swain , Surender Reddy Salkuti
In electric vehicles (EVs), safety measures must be taken to prevent dangerous accidents. Safety regulations must be in place for two important things: electric or EV batteries and EV equipment. Operating an electric vehicle charging stations (EVCS) is a challenging task. This holistic approach is used to evaluate when renewable energy is produced. It's best to focus on the popularity of EVs as more and more people choose this mode of transportation. It is important to know that power plants can be risky. Therefore, safety issues related to EV charging must be addressed quickly and appropriately. Potential safety issues with EVs include overcurrent, ground faults, and overheating. If the charging system does not work, the electric car's battery may heat up and catch fire, and overcharging may cause other problems. To avoid security risks, you must comply with security regulations, use payment devices that meet security requirements, and follow the manufacturer's instructions.
Volume: 14
Issue: 4
Page: 1332-1339
Publish at: 2025-12-01

Artificial intelligence-based multi-key security for protected and transparent medical cloud storage

10.11591/ijaas.v14.i4.pp1241-1250
Ravi Kiran Bagadi , Neelima Santoshi Koraganji , Bandreddi Venkata Seshukumari , Kavya Ramya Sree Karuturi , Sireesha Abotula , Bodapati Venkata Rajanna , Mahalakshmi Annavarapu , Nitalaksheswara Rao Kolukula , Jayasree Pinajala , James Stephen Meka
Ensuring the security and privacy for the patient medical records and medical reports data is a crucial challenge as cloud-based healthcare technologies become more prevalent. For cloud-hosted medical data, internet of things (IoT) and artificial intelligence (AI) technologies shows best solutions for the challenges in the medical domain. This study suggests a Secure and Transparent Multi-Key Authentication Framework that makes use of AI. Using Z-score normalization, the framework first preprocesses the data before clustering to create a multi-level multi-key security structure. The physics-informed triangulation aggregation neural network (PITANN) model in the study reduces computation costs by minimizing overhead, ensuring secure handling of location-based and medical data for enhanced data classification and encryption effectiveness. A multi-key derivation of an elliptic curve, the ElGamal cryptography scheme is presented, which allows for safe multi-key encryption with little increase in the length of the ciphertext. This method guarantees safe, confidential access to cloud-hosted encrypted health information. An envisioned amalgamation improves flexibility by enhancing performance metrics such as speed of computation while safeguarding patient information through enhanced security measures and ensuring precise medical record integrity within virtual healthcare systems.
Volume: 14
Issue: 4
Page: 1241-1250
Publish at: 2025-12-01

Development of a hydraulic jack system bending tool for improved manufacturing efficiency

10.11591/ijaas.v14.i4.pp1072-1082
Muhammad Arsyad Suyuti , Rusdi Nur , Ahmad Nurul Muttaqin , Arminas Arminas , Zainal Sudirman
This article presents the design, fabrication, and testing of a hydraulic sheet metal bending tool. The main objective was to create a tool capable of bending sheets of various thicknesses, ranging from 2 to 4 mm, with high precision and minimal operator effort. The design incorporates a hydraulic ram for easy operation, allowing multiple plates to be bent in a short period of time. Key calculations, including bending force, spring load, and hydraulic force, are performed to ensure the efficiency and safety of the tool. Experimental results show that the tool is able to achieve the desired bending angles, with minimal spring return, and can handle up to three 10 cm wide sheets in approximately 10 minutes. The performance of the tool has been proven by tests, and the results confirm that it can meet the requirements of industrial sheet metal bending. Based on these results, the tool demonstrates its effectiveness in small and medium-scale operations, providing a cost-effective solution for sheet metal production.
Volume: 14
Issue: 4
Page: 1072-1082
Publish at: 2025-12-01

Prediction index drought use neural network based rainfall

10.11591/ijaas.v14.i4.pp1146-1154
Nur Nafiiyah , Ali Mokhtar
Prolonged dry seasons compared to rainy seasons often lead to drought, making drought index observations essential. In Indonesia, drought monitoring commonly uses the standardized precipitation index (SPI), yet there is no common standard for drought index measurement. Therefore, this research applies the Z-score index (ZSI) and China-Z index (CZI), which, like SPI, are rainfall-based drought indices but have rarely been explored in previous research. To predict ZSI and CZI, this research compares the weighted moving average (WMA) and multilayer perceptron (MLP) methods. Two input scenarios are tested: the previous two periods (t-2, t-1) and the previous three periods (t-3, t-2, t-1). The results show that MLP outperforms WMA, with the best performance achieved by the MLP model at a mean absolute percentage error (MAPE) of 4.177% using the three variable input scenario and MLP architecture 3-6-10-1.
Volume: 14
Issue: 4
Page: 1146-1154
Publish at: 2025-12-01

Generative adversarial network for intelligent haze removal from high quality images

10.11591/ijaas.v14.i4.pp1340-1349
Ali Abdulazeez Mohammed Baqer Qazzaz , Hayfaa T. Hussein , Shroouq J. Al-janabi , Yousif Mudhafar
Suspended atmospheric particulates like haze, mist, and fog greatly degrade captured images, creating considerable challenges for computer vision applications operating in safety-sensitive areas such as autonomous driving, surveillance, and remote sensing. In this paper, we treat the important challenge of single-image haze removal by proposing a novel and robust conditional generative adversarial network (cGAN)-based framework. The proposal utilizes a U-Net-based generator with self-attention and skip connections to preserve spatial fidelity, and a PatchGAN discriminator to enforce local realism. At the heart of our contribution is a carefully weighted multi-component loss function that applies reconstruction, perceptual, edge, structural similarity (SSIM), and adversarial losses to optimize pixel-level accuracy and perceptual quality. We trained and evaluated our proposal on the large-scale real-world LMHaze dataset. Experimental results demonstrate state-of-the-art performance with a peak signal-to-noise ratio (PSNR) of 33.42 dB and SSIM of 0.9590. Our qualitative and comparative analyses further support our claims by assessing our proposed model's capacity to recover clear and artifact-free images from hazy images - outperforming the existing methods on this challenging real-world benchmark.
Volume: 14
Issue: 4
Page: 1340-1349
Publish at: 2025-12-01

Li-Fi technology for automated transport

10.11591/ijaas.v14.i4.pp1129-1136
Popuri Rajani Kumari , Chalasani Suneetha , Maddali Anil Kumar , Tangirala Mrudula , Anbumani Venkatachalam , Bodapati Venkata Rajanna , Giriprasad Ambati
India is now one of the countries that is growing quickly worldwide. Today, practically for everything, a vehicle is necessary. Vehicle production is growing rapidly. One of the downsides of this enormous increase is the ineffective management of traffic. The well-planned expansion of transport organizations has resulted in a variety of challenges with travel. It is detrimental to both mankind and the economy when emergency vehicles like ambulances and fire engines are late in arriving. Smart transport is the most effective strategy to lower vehicle accidents and communicate with other cars to open a way for emergency vehicles. Here, the preliminary ideas and findings of a small-scale model of an automated transport system are presented using an innovative discovery known as Li-Fi, also known as light-fidelity. Full duplex communication is accomplished with Li-Fi, in which light is modified at speeds that are too rapid for the eye to follow. Li Fi may be used to create intelligent transportation systems since it offers various advantages over other communication protocols.
Volume: 14
Issue: 4
Page: 1129-1136
Publish at: 2025-12-01
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