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IoT-enabled smart hydroponic system using nutrient film technique for precision agriculture

10.11591/ijict.v15i2.pp900-908
Varuna Kumara , Akshatha Naik , Fatima Tahsir , Sinchana Bommayya Devadiga , Vinitha Ramesh Naik
The study aims to develop an internet of things (IoT)-enabled automated hydroponic system using the nutrient film technique (NFT) to optimize plant growth with minimal human intervention. The system integrates sensors, microcontrollers, and cloud-based monitoring to maintain optimal conditions for crops. The system utilizes Arduino Uno, ESP8266 Wi-Fi module, and sensors including pH, TDS, DHT11 and water level sensors. Data collected from these sensors is processed in real time, allowing automated adjustments through relay-controlled water and nutrient pumps. The system transmits data to the ThingSpeak IoT platform, enabling remote monitoring and predictive analytics. The proposed hydroponic system ensures stable environmental conditions, improving plant growth efficiency. Key parameters such as pH, TDS levels and humidity are maintained within optimal ranges. The automated system reduces manual intervention, enhances water and nutrient efficiency, and increases yield consistency compared to traditional farming methods. The IoT-based NFT hydroponic system demonstrates significant potential in urban agriculture and controlled environment farming. By leveraging automation, AI-driven analytics, and cloud-based monitoring, it provides a scalable and sustainable solution for precision farming. Future advancements may include AI-based predictive analytics, solar-powered energy solutions, and robotic automation for further optimization.
Volume: 15
Issue: 2
Page: 900-908
Publish at: 2026-06-01

Ensemble windows intrusion detection system using XGBoost and deep learning

10.11591/ijict.v15i2.pp565-577
Pranitha Kedambady Shiva , Pushparaj D. Shetty
Intrusion detection systems (IDS) are critical for preserving the Windows environment from an ever-changing collection of cyber threats. Current IDS uses deep learning (DL), which are heavy models if used for detection, while others use machine learning (ML) techniques, which require external feature extraction. To resolve this challenge, this paper introduces XGBNN, a new ensemble model that combines the benefits of ML and DL to identify and mitigate attacks against Windows machines effectively. The various ML methods are trained on the publicly available dataset to classify eight types of attacks in a Windows environment. Additionally, deep neural networks (DNNs) are proposed by optimizing the layers and hyperparameters to achieve the best accuracy. Then, the DNN model and XGBoost model are integrated to detect intrusions by utilizing the feature extraction ability of DNN and providing the intermediate features extracted from the last second layer of the DNN to the XGB for classification. The Ensemble model XGBNN optimizes features and offers better decisions. The proposed model achieves an exceptional accuracy of 100%, as demonstrated by the empirical results, and outperforms the benchmark models with an improvement of 0.004%. The purpose of this study is to highlight the effectiveness of hybrid architectures in intrusion detection. These architectures offer a more robust, scalable, and effective method to improve the security of the Windows system against more sophisticated attacks.
Volume: 15
Issue: 2
Page: 565-577
Publish at: 2026-06-01

Designing a flutter-based community recipe mobile application

10.11591/ijict.v15i2.pp707-718
Nik Ahmad Uzair , Zarina Che Embi
This study focuses on developing a cross-platform mobile application for community-based recipe sharing, addressing the increasing role of mobile technology in daily life. Although recipe apps are globally popular, their adoption in Malaysia remains limited. The proposed application aims to fill this gap by providing users an interactive platform to explore, share, and try new recipes within a cooking-focused community. Key features include personalized recipe suggestions, and an intuitive, easy-to-use interface designed for all devices, enhancing user engagement and promoting community interaction. A background study is conducted to understand the existing landscape and user needs. It is followed by a design phase, which will lay the groundwork for addressing the identified challenges. Based on the insights gained from the background study and design outline, a mobile application is developed, aligning with the analyzed requirements and system design. This paper reports on the design and usability evaluation of this study. Based on the design guidelines, it has been found that this application could provide an intuitive and seamless user experience. Future works include the integration of smart kitchen features and personalized machine learning for better user experience.
Volume: 15
Issue: 2
Page: 707-718
Publish at: 2026-06-01

Android mobile 3D augmented reality engineering devices design using marker-based technique

10.11591/ijict.v15i2.pp683-698
Mohamad Azim Ibrahim , Murizah Kassim , Jasni Mohammad Zain , Suhaili Beeran Kutty , Marina Mohd Yusoff , Barokah Isdaryanti , Farid Ahmadi , Nor Syazwani Mohd Pakhrudin
Engineering teaching and learning utilizing using augmented reality (AR) technologies is crucial with new technology adaptation. This study has developed an Android mobile based augmented reality of engineering device (ARED) with description using marker-based technique. Unity 3D, Vuforia, and Blender Animation were used to design 3D models of engineering devices on AR platforms. ARED is used to scan a marker and display an AR 3D model of engineering devices with its information. Ten engineering devices models were created using Blender Animation Tools and exported to Unity 3D which are Ups Power, Infrared Thermometer, Cisco Router, Multi meter, Poe Switch, Clamp Meter, Power Supply, Arduino Uno, Raspberry Pi and Oscilloscope. ARED mobile app is successfully tested which presents users can interact with the 3D model using touch input to enhance their learning experience. Result presents user’s evaluation analysis at 86.2% of ARED’s effectiveness and impact for future education. The technical analysis shows that ARED can handle the optimum distance range between 35 to 100 cm, operation angle is best between 45 and 135 degrees and occlusion average maximum of 55%. The significance of the research is to improve the quality and process of engineering education by using AR and promotes the learning society’s transition to digital learning with mixed reality in engineering, which creates a borderless learning environment.
Volume: 15
Issue: 2
Page: 683-698
Publish at: 2026-06-01

A novel Lucas-based adaptive sampling optimization for enhancing network lifetime

10.11591/ijict.v15i2.pp607-615
Kanaka Raju Rajana , Shanmuk Srinivas Amiripalli
This paper introduced to enhance network lifetime using a novel Lucas based adaptive sampling methodology by sampling network condition to dynamically modifying sampling intervals using the Lucas sequence, this sequence not only used for sampling but also used to modify data collection, optimizing accuracy and energy efficiency. This technique aims to reduce superfluous data transmissions and conserve network resources by monitoring network utilization and adjusting sample with low medium and high rates. We enhance the network performance and longevity using Lucas based technique via simulation and demonstrating its potential. This may effectively approach novel address to challenges associated with constrained networks, particularly in the domain of IoT and wireless sensor networks (WSNs).
Volume: 15
Issue: 2
Page: 607-615
Publish at: 2026-06-01

Optimized mapping in 2D and 3D network on chip using Bat algorithm

10.11591/ijra.v15i2.pp488-502
Maamar Bougherara , Rafik Amara , Amina Guidoum
Communication within system-on-chip (SoC) architectures has evolved significantly to keep pace with the growing complexity of modern applications. To overcome the limitations of traditional interconnects, network-on-chip (NoC) has emerged as a scalable and efficient communication solution. Although early NoC designs relied heavily on 2D architectures, their physical and performance constraints have led to the rise of 3D NoC architectures, which offer better spatial integration and improved performance. In order to automate the NoC design process, a number of electronic design automation (EDA) tools and optimization algorithms are employed to help designers achieve efficient and high-performance designs. Within this EDA framework, one of the most critical stages is the core placement or application mapping phase, where computational tasks are allocated to the processing elements of the architecture. This step is very hard due to its combinatorial nature, and its optimization is essential since it directly impacts communication cost, energy consumption, and overall system performance. To address this challenge, numerous heuristic and metaheuristic algorithms have been explored for both 2D and 3D NoCs. In this paper, we propose an adaptation of the bat algorithm to solve the mapping problem in both 2D and 3D NoC architectures, with the objective of minimizing communication cost. The proposed approach is evaluated and compared against other optimization methods to assess its effectiveness in enhancing NoC performance within the EDA framework.
Volume: 15
Issue: 2
Page: 488-502
Publish at: 2026-06-01

Real-time emotion prediction system using big data analytics

10.11591/ijict.v15i2.pp869-879
Manpreet Kaur Dhaliwal , Rohini Sharma , Rajbinder Kaur
Emotions are an inseparable part of human existence. Emotions have a big impact on the success and failure of the human race. Comprehending human emotions could prove beneficial in creating improved systems for education, security, market sales, production, healthcare and other areas. Big data analytics applied to streamlined real time emotion sensor’s data can give new insights to anticipate stress before it arises and help in making significant choices that improve people's quality of life. This work proposes a framework for big data management and analysis of GSR sensor’s data in real-time for predicting emotions in human participants. Supervised learning techniques, ensemble boosted tree, neural network, Naïve Bayes, support vector machine, decision tree, K-nearest neighbor, and quadratic discriminant analysis are applied to the collected data. Two distinct criteria have been utilized for testing on real-time data one is trained on all participant data, resulting in a generalized system, while the other is trained on participant-specific data, resulting in a personalized system. Hence, the personalized system achieves an accuracy of up to 80.64% across all classes and 100% for binary classes as compare to generalized system achieves 78.12% accuracy. It is concluded that for the purpose of predicting emotions, the personalized model performs better than the generalized model.
Volume: 15
Issue: 2
Page: 869-879
Publish at: 2026-06-01

Design and development of WIKIN: an interactive nuclear community website for Indonesia using Laravel framework

10.12928/telkomnika.v24i3.27447
Abdelilah; Abdelmalek Essaadi University Mhamedi , Mohammed; Abdelmalek Essaadi University Mghari , Abdelaaziz; Abdelmalek Essaadi University El Hibaoui
Despite its significant contributions to health, agriculture, and energy, the public perception of nuclear technology in Indonesia remains cautious and fragmented. Existing communication channels are largely one-way and regulatory, offering limited opportunities for public interaction and collaborative learning. This study investigates how an interactive web-based platform can enhance public engagement and knowledge sharing in nuclear science and technology. To address this challenge, a Nuclear Community Interactive Website (WIKIN) for Indonesia was designed and developed using the Laravel framework, following a structured waterfall methodology. The system integrates role-based access control, modular architecture, and responsive design to support community participation through the sharing of news, discussions, and documentation of service activities. The evaluation was conducted through black-box functional testing of 27 features (all passed) and a system usability scale (SUS) survey involving 51 users, which produced an average score of 74.8 (“Good”), indicating satisfactory usability and acceptance. These results demonstrate that WIKIN provides an effective model for fostering two-way communication, improving transparency, and strengthening public literacy regarding nuclear issues. This study contributes to digital public engagement research by demonstrating how user-centered design principles can be effectively applied to enhance trust, transparency, and community participation in nuclear science communication.
Volume: 24
Issue: 3
Page: 840-851
Publish at: 2026-06-01

Enhancing image security through nonlinear preprocessing and double random phase encoding using fractional fourier transform

10.12928/telkomnika.v24i3.27650
Fayçal; University of Setif 1 Radjah , Nacira; University of Mohamed El Bachir El Ibrahimi Diffellah , Tewfik; University of Setif 1 Bekkouche , Lahcene; University of Setif 1 Ziet
Image encryption is crucial for secure data transmission in fields such as IoT, medical imaging, and biometrics. This paper proposes an enhanced encryption framework that combines nonlinear preprocessing with double random phase encoding (DRPE) using the fractional fourier transform (FrFT). The diffusion process replaces the conventional XOR operation with a nonlinear hyperbolic tangent (tanh) function, improving confusion diffusion complexity and resistance to cryptanalytic attacks. Experimental results show a reduction in peak signal-to-noise ratio (PSNR) from 9.03 dB to 7.25 dB and a mean squared error (MSE) increase to 10×10³, indicating stronger encryption and lower correlation with the original image. The proposed method also enhances robustness against histogram and key sensitivity attacks. Statistical analyses, including entropy and number of pixels change rate (NPCR) metrics, demonstrate that the approach outperforms conventional DRPE methods while maintaining computational efficiency. This hybrid nonlinear and FrFT-based framework provides a practical and scalable solution for secure image transmission in sensitive and real-time applications.
Volume: 24
Issue: 3
Page: 1003-1013
Publish at: 2026-06-01

Cascaded generalized predictive control for induction drives under constraints using particle swarm optimization

10.11591/ijra.v15i2.pp445-457
Rachid Amrouche , Noureddine Boumalha , Farid Ykhlef , Djilali Kouchih
This paper presents a cascaded generalized predictive control (CGPC) strategy for induction motor drives under operational constraints, optimized through particle swarm optimization (PSO). In the proposed scheme, the outer loop regulates the motor speed, while the inner loop controls torque and flux, ensuring accurate multi-level regulation. PSO is employed to optimally tune the prediction horizon and weighting factors, enhancing robustness, transient response, and disturbance rejection. Unlike conventional GPC–PSO approaches that neglect explicit constraint handling, and linear matrix inequalities (LMI)-based model predictive controller (MPC) methods that impose high computational costs, the proposed CGPC–PSO achieves both constraint management and real-time efficiency. Moreover, compared with Neural-MPC strategies that require retraining for each system, the proposed method provides generalizable and adaptive control without sacrificing computational performance. Simulation results validate the effectiveness of the approach, demonstrating superior trajectory tracking, robustness against parameter variations, and improved dynamic performance compared with RST, LMI, and neural-MPC controllers. These findings position CGPC–PSO as a promising candidate for advanced induction motor drive applications.
Volume: 15
Issue: 2
Page: 445-457
Publish at: 2026-06-01

An machine learning-enhanced reconfigurable software defined radio architecture for adaptive 5G wireless systems

10.11591/ijict.v15i2.pp699-706
Vijaya Bhaskar Chalampalem , Sancarapu Nagaraju , Venkata Vara Prasad , R. Kiran Kumar , Shanmugham Balasundaram
This paper presents a machine learning (ML)-enhanced software defined radio (SDR) architecture optimized for adaptive 5G wireless communication. The system incorporates predictive ML algorithms to enable real-time modulation selection, finite impulse response (FIR) filter reconfiguration, and spectrum adaptation based on dynamic channel parameters such as bit error rate (BER), received signal strength indicator (RSSI) and signal-to-noise ratio (SNR). A decision tree classifier and a deep Q-learning agent dynamically determine optimal modulation schemes (BPSK, QPSK, 16-QAM, OQAM) and filter tap configurations (4/8/16 taps), ensuring efficient communication under varying network conditions. Implemented on a Xilinx Zynq SoC using Verilog for datapath design and Python for ML control via AXI4-Lite, the architecture achieves a maximum operating frequency of 182.4 MHz, 40.7% logic utilization, and only 122.3 mW power consumption. Compared to existing SDR implementations, the system demonstrates a 17% frequency improvement, 28% power reduction, and 21% area savings. Real-time electrocardiogram (ECG) transmission confirms the system’s adaptability, achieving BER < 10⁻³ at 22 dB SNR and < 10⁻⁵ at 26 dB. These results affirm the viability of the proposed ML-SDR for edge-based biomedical and ultra-reliable low-latency communications (URLLC) applications in 5G networks.
Volume: 15
Issue: 2
Page: 699-706
Publish at: 2026-06-01

Deep learning-based optimization techniques for network lifetime enhancement in wireless sensor networks

10.11591/ijict.v15i2.pp623-633
Abhay Raghunath Gaidhani , Amol D. Potgantwar
Wireless sensor networks (WSNs) are integral to applications like environmental monitoring, healthcare, and surveillance, yet they face the critical challenge of limited energy resources, which shortens the network's operational lifespan. Addressing this issue, this paper explores deep learning-based optimization techniques as a solution to enhance network lifetime by efficiently managing energy consumption. We present a detailed review of the existing challenges in WSNs and examine various deep learning methods, including neural networks, deep reinforcement learning (DRL), and generative adversarial networks, specifically tailored for WSN optimization. In this study, we introduce a new reinforcement learning (RL) based optimization algorithm to prolong the network lifetime. The proposed technique is intended to smartly distribute the energy consumption among the network elements, leading to desirable performance with regard to the battery lifetime. The paper ends with a summary of design aspects and future research directions to improve the WSN performance further based on deep learning.
Volume: 15
Issue: 2
Page: 623-633
Publish at: 2026-06-01

A review of sensemaking design elements: towards an affordances typology

10.11591/ijict.v15i2.pp488-496
Fadzlin Ahmadon , Murni Mahmud , Muna Azuddin
This study explores the intersection of interaction design and sensemaking within digital systems, aiming to identify and categorize key affordances that enhance user sensemaking. Starting with a focused literature review, key design elements such as tagging and annotation are identified, important for effective sensemaking in interaction design. Drawing on Maier's construct of affordances, the behaviours of these design elements are analyzed to derive specific affordances integral to enhancing user experience. The primary objective is to develop a generalized affordance typology that supports sensemaking across various digital systems. This typology organizes the derived affordances into broad themes such as effortless discovery, expressive freedom, collaborative engagement, cognitive support, insight enhancement, and user empowerment. This typology serves as a tool for interaction designers, facilitating the application of these themes in various design scenarios to create more intuitive and effective digital environment for sensemaking.
Volume: 15
Issue: 2
Page: 488-496
Publish at: 2026-06-01

Machine learning centered energy optimization in mobile edge computing: a review

10.11591/ijict.v15i2.pp465-476
Chandapiwa Mokgethi , Tshiamo Sigwele , Kabo Clifford Bhende , Aone Maenge , Selvaraj Rajalakshmi
Current literature reviews on machine learning-based approaches for mobile edge computing (MEC) energy optimization often lack in-depth gap analysis and fail to identify trends or offer actionable insights. Most focus narrowly on comparing MEC frameworks without critically evaluating or benchmarking prior research. This review contributes by addressings these gaps via analysis of existing reviews and related studies, with a focus on ML models, research objectives, evaluation metrics, datasets, tools, and gap identification. The review method follows a systematic literature review (SLR) using the PRISMA framework for transparency and reproducibility. Key findings reveal persistent challenges in energy consumption, computational overhead, cost, and poor performance in accuracy, QoS, latency, scalability, and carbon footprint. Deep reinforcement learning (DRL) emerges as the most commonly used model (55%), while TensorFlow (35%) is the most adopted tool, valued for its flexibility and robust community support. The AudioSet dataset is frequently used (28%) due to its compatibility. However, methodology limitations include dependency on study quality and exclusion of grey literature, context sensitivity. The review concludes by recommending advanced solutions such as serverless computing, liquid cooling, containerization, software-defined power, quantum computing, and blockchain to drive future MEC energy optimization.
Volume: 15
Issue: 2
Page: 465-476
Publish at: 2026-06-01

A new modified characteristic equation for optimal coordination of directional overcurrent relays

10.11591/ijict.v15i2.pp789-796
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
The integration of distributed generation (DG) into power systems is increasing to meet the requirements of the utility system. Renewable energy sources are given priority due to their clean energy and high consistency advantages. Integration of DG into the system makes the bi-directional flow of current. Directional type overcurrent relays are usually used for protection of lines associated with bidirectional power flows. The installation of DGs, (especially, inverter-based) invites challenges to the existing protection schemes. A new modified characteristic equation-based approach is proposed in this paper to obtain the faster operational time of relays. The relay coordination scheme proposed in this paper is applied to an 8-bus test system integrated with the solar-based photovoltaic integrated distributed generator (PVIDG). The comparative analysis between the conventional and proposed approaches is done.
Volume: 15
Issue: 2
Page: 789-796
Publish at: 2026-06-01
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