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Optimal battery sizing using modified spider monkey optimization in grid connected microgrids

10.11591/ijra.v14i3.pp356-365
Meraj Fatima , Manne Rama Subbamma
Microgrids (MGs) must have optimally sized storage and renewable energy sources to operate efficiently, economically, and reliably. MG may benefit from optimization techniques in their scheduling and sizing since they have a variety of energy sources with varying availability conditions and necessary costs. In this research, a novel modified spider monkey-based energy management system (MSM-EMS) has been proposed by increasing the photovoltaic (PV) or battery energy storage system (BESS) module capacity while minimizing grid connectivity dependency. The fundamental idea behind the proposed approach is greater dependability at the lowest feasible cost. By taking into account the BESS utilization factor and PV forced outage rates in a MG, the method becomes more realistic. Despite the absence of renewable energy sources and the grid, the proposed strategy provided critical loads according to schedule while maintaining reserve margins. Experimental findings demonstrate that the modified spider monkey optimization (MSMO)-based algorithm can determine the best BESS size and PV depending on cost. In comparison to particle swarm optimization (PSO) of $2756.1 and ABC of $2912.65, the ideal cost for EMS-MSMO is $2215.77 which is relatively low compared to the existing technique. As a result, the suggested MSMO algorithm and innovative energy management system has been optimized along with PV and battery dimensions.
Volume: 14
Issue: 3
Page: 356-365
Publish at: 2025-12-01

Harvesting insights: exploring machine learning for crop selection and predictive farming

10.11591/ijaas.v14.i4.pp999-1009
Tanvi Deshmukh , Anand Singh Rajawat , Amol Potgantwar
Modern agriculture has undergone a significant evolution, adopting advanced techniques to streamline crop management processes. One such advancement is the integration of machine learning (ML) technology, which shows great promise in optimizing crop selection and enhancing economic returns. Key determinants of crop productivity, including water availability, soil quality, weather conditions, and timely resource allocation, play pivotal roles in the farming ecosystem. Harnessing these factors, ML algorithms facilitate the identification of optimal crop choices and provide continuous monitoring of cultivation processes to anticipate evolving crop needs. This paper investigates various ML techniques employed for crop selection and evaluates their effectiveness in agricultural settings. Through a comparative analysis, we highlight the advantages of these techniques and provide insights into their potential impact on current farming management practices. By leveraging ML for predictive farming, stakeholders can make informed decisions to maximize yields, minimize resource wastage, and promote sustainable agricultural practices. This study contributes to the ongoing discourse on the integration of technology in agriculture and underscores the transformative potential of ML in shaping the future of crop management. We investigate recent papers from the years 2020 to 2024.
Volume: 14
Issue: 4
Page: 999-1009
Publish at: 2025-12-01

A hybrid transformer-graph neural networks framework for enhanced physical activity recognition and sedentary behavior analysis

10.11591/ijra.v14i3.pp429-438
Sudarsanam Anandanarayanan , Suvarnalingam Thirumaran
Sedentary behavior has been identified as a major risk factor for chronic diseases such as cardiovascular disorders, obesity, and diabetes. The accurate prediction of sedentary health risks is essential for early intervention and personalized healthcare strategies. This study proposes a novel machine learning-based predictive model that leverages transformer-based architectures and graph neural networks to analyze multidimensional behavioral data. Unlike traditional models, our approach incorporates temporal attention mechanisms to capture long-term dependencies in activity patterns and graph-based learning to model complex relationships between physiological and behavioral factors. The study utilizes real-world datasets, including wearable sensor data and self-reported activity logs, to train and validate the models. Experimental results demonstrate that the proposed framework outperforms conventional machine learning techniques such as random forest and XGBoost, achieving superior predictive accuracy and robustness. The findings highlight the potential of advanced machine learning algorithms in assessing sedentary health risks, enabling proactive health management and intervention strategies.
Volume: 14
Issue: 3
Page: 429-438
Publish at: 2025-12-01

FIND-ROUTE: Fourier series integrated deep learning model for energy efficient routing in Internet of Things-wireless sensor network

10.11591/ijra.v14i4.pp620-630
Shobanbabu Ramaswamy Jaganathan , Sathya Rajendran , Karthikeyan Ramamoorthy
The Internet of Things (IoT) relies on wireless sensor networks (WSNs) to transmit data across a wide range of applications. However, the commonly encountered primary challenges in IoT-enabled WSNs are high energy consumption during data transmission, which insists energy optimized routing to prolong the network lifetime. To address these challenges, a novel Fourier series integrated deep learning-based routing (FIND-ROUTE) framework has been proposed for energy-aware communication among IoT nodes in WSN. Initially, a hybrid clustering approach forms an adaptive cluster for efficient data aggregation with reduced energy consumption. After clustering, stable cluster heads (CHs) are elected by a Fourier series-based metaheuristic optimization algorithm for balancing the energy usage with extended network lifetime. Finally, an Intelligent neural network dynamically selects the optimal path and transmits the data efficiently with reduced latency for reliable communication in IoT-WSN. The FIND-ROUTE framework is simulated by using MATLAB, and it is validated by using the WSN-DS dataset. The proposed FIND-ROUTE framework is evaluated based on several parameters, including energy consumption, packet delivery ratio (PDR), network lifetime (NL), time complexity, throughput, number of alive nodes, packet loss ratio (PLR), and space complexity. In comparison, the proposed FIND-ROUTE framework achieves a PDR of 90%, whereas MLBDARP, LQEER, and NBSHO-DRNN achieve 70%, 60%, and 67% respectively.
Volume: 14
Issue: 4
Page: 620-630
Publish at: 2025-12-01

Cloud-based secure data storage in healthcare using elliptic curve cryptography

10.11591/ijaas.v14.i4.pp1281-1294
Gayathri Govindappa Nalina , Channakrishna Raju
The growth of cloud computing in the healthcare field has led to significant developments, but ensuring the confidentiality and protection of medical records such as electronic health records (EHRs) remains a major concern for healthcare service applications. In cloud computing, the basic authentication provided by most service providers is insufficient to ensure secure access to critical or sensitive resources. Moreover, most of the existing healthcare management systems are ineffective in handling a number of patient data, which leads to single points of failure. To address these issues, elliptic curve cryptography (ECC) with Curve25519 is utilized to enhance security in cloud storage, particularly within healthcare management systems. The ECC with Curve25519 is optimized for efficient and fast scalar multiplication, which reduces computational overhead and enhances performance. The curve parameters are selected to prevent vulnerabilities and ensure security against known attacks. Moreover, it is efficient in maintaining the integrity of patient records, which reduces storage and bandwidth requirements. The ECC with Curve25519 achieves lower Key-Gen, prove, verify, proving key size, and verification key size of 13.7 s, 48 s, 0.608 s, 13.27 Mb, and 123.70 Kb, respectively, in comparison with proxy re-encryption algorithm with zero-knowledge proof (ZKP).
Volume: 14
Issue: 4
Page: 1281-1294
Publish at: 2025-12-01

Design and implementation of QUADRESCUE: A ROS-based quadruped robot for disaster response support

10.11591/ijra.v14i3.pp387-398
Sanjay Deshmukh , Ojas Chanakya , Om Gabani , Kashish Patni , Asmita Deshmukh
Search and rescue (SAR) operations in hazardous environments demand robotic systems capable of traversing complex terrains while ensuring responder safety. Traditional wheeled platforms often fail in debris-laden areas, and fully autonomous quadrupeds remain financially out of reach for many rescue agencies. This paper presents the design and development of QUADRESCUE, a modular operator-assisted quadruped robot built to bridge the gap between affordability and capability in disaster response. QUADRESCUE delivers core SAR functionalities including remote visual inspection, real-time terrain mapping via an RGB-D camera, payload transport, and GPS-based survivor localization. Built with a robust three degrees of freedom (3DoF) per leg design, the robot uses inverse kinematics algorithms to precisely control twelve servo motors for stable locomotion across uneven terrain. The system integrates the robot operating system (ROS) for seamless operation, real-time joystick control for easy navigation, an IMU for orientation sensing, and a GPS module with 3-meter accuracy. Field evaluations demonstrate 80–94% success rates on challenging surfaces, substantially outperforming wheeled counterparts 19% to 39% with a 200-meter control range and 45 minutes of runtime. QUADRESCUE offers a lightweight, cost-effective, and repairable solution that combines practical usability with advanced performance, making it well-suited for real-world deployment in emergency rescue situations.
Volume: 14
Issue: 3
Page: 387-398
Publish at: 2025-12-01

Design and implementation of an internet of things-based automatic waste sorting system

10.11591/ijaas.v14.i4.pp1155-1165
Akhmad Taufik , Paisal Paisal , Muhammad Ruswandi Djalal , Zahran Atha Dillah , Haryono Ismail
This paper presents the design and development of an internet of things (IoT)-based automatic waste sorting system that classifies waste into four categories: organic, non-organic, metal, and others. The system integrates an Arduino Mega for control, multiple proximity sensors (inductive, capacitive, and infrared), and ultrasonic sensors for level detection, and a NodeMCU ESP8266 for real-time monitoring via the Blynk platform. A total of 100 tests (25 per bin) were conducted. Classification success rates were 92% (metal), 80% (inorganic), 84% (organic), and 100% (others), resulting in an overall accuracy of 89%. The main contribution is a combined automatic sorting and IoT monitoring framework suitable for campus-scale deployment.
Volume: 14
Issue: 4
Page: 1155-1165
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

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

SHIELD: Security based hybrid autonomous deep learning network for load balancing in cloud

10.11591/ijra.v14i3.pp439-449
Loga Priyadarshini Kathirmalaiyan , Nithya Muthu
Load balancing in the Internet of Things (IoT) enhances the efficiency of the system by dynamically allocating tasks across devices and cloud resources. However, task scheduling struggles with unpredictable tasks, scalability, security risks, and unauthorized access control. To overcome these limitations, a novel security-based hybrid autonomous deep learning network for load balancing in cloud (SHIELD) framework has been proposed for secure task scheduling in cloud resources. Initially, the data received from the IoT devices is passed under certain security constraints to ensure the authenticity of the data. These privacy-preserved data are fed to the task scheduling module, which is employed by the dual DL Network to generate a schedule for resource management. Finally, cloud resources employ optimal allocation of tasks based on the generated schedule to ensure secure load balancing. The proposed framework is simulated by using Cloud Simulator 7G (CloudSim7G). The SHIELD framework is assessed by such metrics, including accuracy, recall, precision, F1-score, and specificity. In comparison, the proposed SHIELD framework achieves a privacy overhead of 14% outperforms the existing QODA-LB, Best-KFF, SPSO-TCS, and VMMISD techniques by achieving 10%, 11%, 12%, and 13% respectively.
Volume: 14
Issue: 3
Page: 439-449
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

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

Investigating relationships between reading comprehension and oral reading fluency through AI-driven tool reading progress

10.11591/ijaas.v14.i4.pp1192-1199
Pham Duc Thuan , Pham Thi Tam
This study investigates the relationship between reading comprehension and oral reading fluency components—accuracy and rate—among 113 Vietnamese EFL university students using the AI-powered tool Microsoft Reading Progress. Over 14 weeks, students engaged in weekly oral reading and comprehension tasks using integrated Microsoft Teams features. Fluency metrics (accuracy and rate) and comprehension scores were automatically collected and analyzed using Pearson correlation. The results revealed weak but statistically significant positive correlations between reading comprehension and accuracy (r = .257, p < .01), and between comprehension and rate (r = .289, p < .01), suggesting that improvements in fluency modestly support comprehension. A strong correlation between accuracy and rate (r = .765, p < .01) was also observed. The study highlights the effectiveness of Reading Progress in capturing fluency data and promoting self-paced improvement. However, limitations such as the short duration, localized sample, and constraints of accent recognition in AI-based speech analysis affect the generalizability and validity of results. The findings support the pedagogical integration of AI tools in EFL instruction while calling for future research with larger samples, extended timelines, and diversified digital tools to further validate and expand on these results.
Volume: 14
Issue: 4
Page: 1192-1199
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

Mobile robot replacement in multi-robot fault-tolerant formation

10.11591/ijra.v14i3.pp311-319
Ahmed M. Elsayed , Mohamed Elshalakani , Sherif Ali Hammad , Shady Ahmed Maged
Formation control in multi-robot systems (MRS) is essential for collaborative transport, environmental surveillance, material handling, and distributed monitoring. A major challenge in MRS is maintaining predefined formations or cooperative task execution when individual robots experience operational faults, potentially isolating them from the group. In mission-critical scenarios, preserving the number of operational robots is crucial for task success. To address this, we propose a Robot Replacement approach framework for differential wheeled mobile robots. This approach isolates faulty robots and dynamically replaces them with pre-deployed spares, ensuring uninterrupted formation tasks. A graph theory-based framework models inter-robot communication and formation topology, enabling decentralized coordination. The proposed techniques were implemented in a MATLAB/Simulink simulation environment. The simulated robots are equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders for navigation. Simulation results demonstrate that the framework successfully maintains the target formation and task continuity during robot failures by dynamically integrating replacements with minimal disruption.
Volume: 14
Issue: 3
Page: 311-319
Publish at: 2025-12-01
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