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Vector logic for robotic system on chip design and test

10.11591/ijra.v15i2.pp415-426
Vladimir Hahanov , Svetlana Chumachenko , Eugenia Litvinova , Andrii Voronov , Oleh Demchenko , Nataliya Maksymova
Artificial Intelligence and vector logic of computing do not contradict but cooperate and enrich each other. Logic is the law of existence and development of emerging computing. Logic is functions and structures, models and algorithms, phenomena and processes. Any computing, including artificial intelligence, is logic and nothing else. Emerging computing devices today have hundreds of systems on a chip and memory blocks, which are interconnected by thousands of connecting wires. This encompasses all the logic, functionalities, and structures, which are subject to testing by system methods. To achieve this, a logic vector serves as a generic form for describing functions, structures, and buses in modeling for the simulation of test sets and logic faults as address. Chip-let Interconnect bus is also a logical functionality or structure. They must be tested to diagnose defects by system logic mechanisms. The latter involves modeling to automatically obtain data structures, followed by good-value simulation and simulation of all fault combinations, such as addresses, on the buses segment. For this purpose, vector logic is used to describe functionalities and structures, models and algorithms, faults and tests. Mechanisms and application that assume a harmonious relationship between the model and the algorithm for their processing are considered.
Volume: 15
Issue: 2
Page: 415-426
Publish at: 2026-06-01

Sustainability and strategic development of biogas generated from tofu manufacturing wastewater

10.11591/ijape.v15.i2.pp831-844
Hashfi Hawali Abdul Matin , Sapta Suhardono , Prabang Setyono , Glora Ramadhani , Yoyon Wahyono , Budiyono Budiyono
Tofu is a widely consumed soy-based food in Indonesia, and its liquid waste is utilized for biogas in Sambak Village, addressing both renewable energy and waste management issues. However, the system's sustainability faces challenges. This research aimed to assess the current sustainability status of the tofu-wastewater-based biogas system and formulate strategic measures to optimize its long-term continuity. Sustainability was analyzed across five dimensions (ecological, economic, social, technological, and institutional) with multidimensional scaling (MDS) method, while strategies were formulated using SWOT analysis. The results showed an overall moderately sustainable system with an index score of 74.15. The ecological, economic, and social dimensions were rated very sustainable, while the technological dimension was quite sustainable, and the institutional dimension was less sustainable. The top priority strategy identified is the development and innovation of biogas installations. While biogas offers significant environmental and social economic benefits, sustainability is hindered by limited biogas volume and weak institutional management. Therefore, guidance, regular monitoring involving all stakeholders, and future supply-demand forecasting are crucial for its long-term viability.
Volume: 15
Issue: 2
Page: 831-844
Publish at: 2026-06-01

Robust efficient ego-vehicle path prediction based on Bezier curves for autonomous driving

10.11591/ijra.v15i2.pp427-444
Hanan H. Hussein , Ahmed Atef , Mohamed Hanafy Radwan
Accurate ego-vehicle path prediction is essential for safety-critical functions in advanced driver assistance systems (ADAS), such as automatic emergency braking (AEB) and collision avoidance. Existing models based on Clothoid curves are typically not sufficient in expressing complex maneuvers and are not highly adaptive to various vehicle dynamics. In addition, these models struggle with accuracy in circular maneuvers and fail to use in complex paths (e.g., S-shapes). This paper proposes a novel representation of the ego-vehicle path prediction using Bezier curves. The proposed Bezier curves are composed of two Cartesian third-order polynomial functions. They are formulated efficiently to model both circular and S-shaped trajectories with high accuracy and low computational cost. Our method significantly reduces prediction error, achieving over 95% improvement in average Euclidean distance error compared to Clothoidal models along about 50 m paths in controlled circular scenarios. The proposed algorithm, designed with O(n) complexity, is suitable for real-time applications on low-power automotive hardware. Its effectiveness is demonstrated through simulation using CarMaker, and a collision estimation module for AEB is developed based on the predicted paths.
Volume: 15
Issue: 2
Page: 427-444
Publish at: 2026-06-01

Design of beefsteak tomato harvesting robot system in greenhouse

10.11591/ijra.v15i2.pp353-364
Thien An Dinh , So Nam Phung , Tri Cong Phung
One challenge for tomato harvesting robots is that some of the tomato stems were not detectable because they were hidden behind the leaves or other obstacles. The primary objective of this research is to design, simulate, and experiment with a tomato harvesting robot and propose an improved detection algorithm to overcome the above problem. The suggested detection algorithm is designed to first detect the tomato fruit itself, and if the stem is not visible, the system will automatically adjust the camera's viewing angle to provide a better perspective and uncover the hidden stem. Simulation and experimental tests were carried out in a real tomato greenhouse to evaluate the cutting and holding mechanism, as well as the camera-based detection algorithm. These experimental results confirmed the effectiveness of the gripper and detection system and revealed several challenges in the harvesting algorithm. By integrating advanced algorithms for tomato detection and harvesting, this robot will reduce damage to the tomatoes, ensuring higher quality and yield.
Volume: 15
Issue: 2
Page: 353-364
Publish at: 2026-06-01

Early prediction of myocardial infarction using proposed score tree algorithm

10.11591/ijict.v15i2.pp813-822
Nusrat Parveen , Utkarsha Pacharaney , Gayatri Hegde , Mohammad Rafique , Sana Firoj Nalband , Shamim Akhtar , Satish Devane
Early detection and diagnosis of a diseases will have a big impact on the medical field and help to prevent loss of life. This study begins by gathering information on myocardial infraction patients from hospitals and focuses on earlier diagnostics. In fact, the pre-processed, confirmed data from a qualified doctor is used for this research. Early prediction of myocardial infarction (MI) is proposed by many researchers. They have used Kaggle datasets that is not recent, and they work on post MI. We have proposed early myocardial infraction detection works on unsupervised datasets. To identify myocardial infraction, numerous machines learning supervised algorithms, including decision tree (DT), random forest (RF), are employed in the literature. In this study, we use the score tree algorithm (STA), which operates on an unsupervised dataset, to present a unique early MI prediction method.
Volume: 15
Issue: 2
Page: 813-822
Publish at: 2026-06-01

Integrating artificial intelligence and Internet of Things for solid waste management: a review

10.11591/ijra.v15i2.pp388-396
Aditya Karle , Tejas Ramdas Pagare , Mohammed Ashaz Arkati , Prathmesh Prafull Tarapurkar , Praveen Kumar Bhojane
The increasing pace of urbanization and industrial growth has intensified the challenges of solid waste management, demanding intelligent, data-driven, and sustainable solutions. This review explores how the combined application of artificial intelligence (AI) and the Internet of Things (IoT) is revolutionizing conventional waste management practices into intelligent, automated, and responsive systems. Through a comprehensive review of 43 scholarly publications, case analyses, and technical studies, this paper emphasizes how AI-based methods—such as learning algorithms, image recognition, and data-driven prediction—improve waste sorting precision, recycling performance, and material recovery efficiency-enhance waste segregation accuracy, recycling efficiency, and resource recovery. Simultaneously, IoT-based systems employing sensors, cloud platforms, and smart bins enable real-time waste monitoring, dynamic routing, and optimized collection logistics. Emerging technologies like blockchain for waste traceability, robotics for automated sorting, and advanced analytics for decision-making are also examined. Despite these advancements, challenges related to scalability, interoperability, cost, and data privacy persist. This review identifies current research gaps, proposes future directions, and emphasizes the importance of integrating AI and IoT with circular economy principles under Industry 5.0 to achieve sustainable, efficient, and human-centric waste management solutions.
Volume: 15
Issue: 2
Page: 388-396
Publish at: 2026-06-01

Hybrid LUT–CORDIC architecture on FPGA for efficient and accurate trigonometric computation in robot manipulators

10.11591/ijra.v15i2.pp377-387
Nia Gella Augoestien , Jazi Eko Istiyanto , Ahmad Ashari , Andi Dharmawan
Although computational resources on robots are often limited, real-time, accurate computation of trigonometric functions is essential in robot manipulators, particularly for forward and inverse kinematics, dynamic analysis, trajectory planning, and motion control. The LUT method requires a large number of LUTs to improve accuracy. The accuracy of the CORDIC method is highly dependent on the number of computational latencies, which affects the computation speed. This paper combines two general approaches for computing trigonometric functions on robot manipulators that improve accuracy without increasing resource utilization and computational latencies. The design uses a 10-bit format (0.125° input resolution and 2-10 output precision) and is implemented in VHDL on a Xilinx Artix-7 XC7A100T-CSG324 FPGA. Compared with a CORDIC-only baseline, the maximum absolute error is reduced from 0.083007812 to 0.009801151 for sine and from 0.079101563 to 0.008901377 for cosine, while MSE drops from 2.4031×10-4 and 2.32974×10-4 to 5.87754×10-6 and 5.87862×10-6, respectively. The hybrid core also reduces slice usage from 81 to 69 and shortens computation time from 35.271 ns to 30.627 ns, making it suitable for resource-constrained real-time robotic control.
Volume: 15
Issue: 2
Page: 377-387
Publish at: 2026-06-01

Fuzzy integral fault-tolerant control of an activated sludge process

10.11591/ijra.v15i2.pp473-487
Ahmed Sami Hamana , Mounir Bekaik , Messaoud Ramdani
This paper presents a fuzzy integral fault-tolerant controller (FIFTC) for robust regulation of substrate and dissolved oxygen in activated sludge processes (ASP). The nonlinear dynamics of the process are represented using an augmented Takagi–Sugeno (TS) fuzzy model, which includes an additional vector representing the integral state to improve tracking accuracy. A fuzzy proportional-integral (PI) observer is employed to estimate states and detect actuator faults, particularly in the aeration system. Controller and observer gains are computed by solving linear matrix inequalities (LMIs), while an H∞ performance criterion, defined by the parameter, ensures effective disturbance attenuation and bounds the error energy. In the simulation, we considered actuator faults of the loss of effectiveness (LOE) type. Simulation results demonstrate that FIFTC significantly outperforms classical linear quadratic regulator (LQR) in terms of tracking accuracy, robustness, and fault tolerance, even under partial actuator failures and external disturbances. The proposed FIFTC control strategy, which leverages fuzzy modeling, robust observers, and LMI-based optimization, provides significant benefits, primarily by improving efficiency, reducing energy consumption, and enhancing robustness.
Volume: 15
Issue: 2
Page: 473-487
Publish at: 2026-06-01

Enhanced transfer learning framework for brain tumor detection from MRI scans using attention-based feature fusion

10.11591/ijict.v15i2.pp497-507
Smita Bharne , Ekta Sarda , Shamal Salunkhe
Due to the complexity of the different tumor types in medical imaging detection of brain tumor is still as prominent challenge. This paper present the innovative technique enhanced transfer learning framework (ETLF) which integrating the advanced pre-processing with hybrid fine-tuned method for accurate brain tumor detection from magnetic resonance imaging (MRI) scans. The proposed model combine the strength of pre-trained convolutional neural networks (CNNs) such as EfficientNetB0 through domain specific transfer learning and attention based fine tuning. A novel feature fusion layer and adaptive learning rate scheduler are key indicators for model performance and prevent overfitting. The methodology is assessed on the benchmark dataset BraTS and Kaggle brain tumor datasets. The main contribution of work lies in development of domain- adaptive transfer learning with different datasets. The ETLF shows the high accuracy of 98.76% which able outperforms effectively in diagnosing tumor suitable of clinical purpose.
Volume: 15
Issue: 2
Page: 497-507
Publish at: 2026-06-01

Tree diameter at breast height measurement based on computer vision

10.11591/ijra.v15i2.pp458-472
Mohamad Razmil Abdul Rahman , Ishak Suleiman , Mohammed Al Haek , Yee Kit Chan
Diameter at breast height (DBH) is a crucial metric in forestry, serving as a key input for estimating timber volumes and biomass, assessing forest health, and aiding in biodiversity and climate change studies. However, traditional measurement methods practiced today are time-consuming and labour-intensive, while many advanced methods introduced in recent years require high upfront costs, limiting wide adoption by small-scale institutions and projects. This research paper aims to explore innovative approaches to DBH measurement that balance accuracy with cost-effectiveness, ultimately contributing to the broader goals of sustainability and environmental protection. In this paper, the authors propose an automated DBH measurement method, extracting the value from smartphone RGB images through the utilization of computer vision techniques and mathematical algorithms. By incorporating tree distance data in Phase 3 of the study, the proposed method achieved accuracy comparable to manual tape measurements while significantly reducing the time and resources required for fieldwork. Specifically, 74 out of 143 trees (51.7%) had an estimated DBH that fell within 1 cm of the actual measurements, resulting in an absolute mean error (MAE) of 1.10 cm, root mean square error (RMSE) of 1.80 cm, and relative root mean square error (RRMSE) of 6.0%. Thus, this hybrid approach offers a promising solution for forestry applications, enhancing both the efficiency and accessibility of DBH data collection.
Volume: 15
Issue: 2
Page: 458-472
Publish at: 2026-06-01

A systematic mapping study: exploring islamic inheritance in computing research

10.11591/ijict.v15i2.pp597-606
Ghader Reda Kurdi
Islamic inheritance, a fundamental component of Islamic jurisprudence governing asset allocation among heirs, presents challenges due to its complexity. Accessible resources are crucial to address these challenges, with computational technologies offering promising solutions. This systematic mapping study provides a comprehensive overview of research at the intersection of computing and Islamic inheritance, comprising 20 studies identified primarily through snowballing. It analyses publication trends, identifies primary application domains, explores computational technologies utilized, assesses empirical evaluation methods, and uncovers gaps, challenges, and limitations in the existing literature, ultimately determining areas necessitating further research. The findings suggest a significant presence of researchers from Southeast Asia, predominantly with backgrounds in computing. The studies focused on the computation of wealth distribution, employing various computational technologies. Furthermore, the findings emphasise the importance of interdisciplinary collaboration and empirical evaluation to enhance technological solutions in this domain.
Volume: 15
Issue: 2
Page: 597-606
Publish at: 2026-06-01

Stacking of machine learning classifiers for bot detection using account level data

10.11591/ijict.v15i2.pp477-487
Jwala Sharma , Samarjeet Borah
Social media is a platform for individuals to connect, share, and create information. Social bots produce automated content and interact with humans; in the process, they learn and mimic humans’ behaviour. This research study addresses the challenge of identifying social media bots (SMB) that can rapidly disseminate information or misinformation on platforms like Twitter. It contributes to the field by reviewing literature to define bot behaviours and exploring advanced machine learning classifiers for effective bot detection using account-level data. The study employed Spearman's rank correlation coefficient to select relevant features for SMB classification, then trained six different machine learning models: decision tree (DT), random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighbour (KNN). To further improve accuracy, a classifier stacking technique was applied. Key findings revealed that while individual classifiers performed variably, with RF leading at 89% accuracy, the stacked classifier approach outperformed all single-classifier methods with an impressive 90% accuracy rate. The results underscore the potential of combining multiple classifiers to enhance the precision of social media bot detection efforts.
Volume: 15
Issue: 2
Page: 477-487
Publish at: 2026-06-01

Exploring player interaction and team cooperation in MMOG playability enhancement

10.11591/ijict.v15i2.pp644-654
Gong Xiaoxue , Lili Nurliyana Abdullah , Azrul Hazri Jantan , Noris Mohd Norowi , Fatimah Sidi , Gulmira Abildinova
The massively multiplayer online games (MMOGs) continue to grow in popularity, and it has become particularly important to understand the key factors that influence team playability. While existing research has focused primarily on system functionality and individual player experience, insufficient attention has been paid to the role of team dynamics in player satisfaction. This study focuses on the core variables that influence team playability, including teamwork, task dependency, team loyalty (TLO), and team relationships (TR), and explores how these variables work together to influence player experience. This study used a combination of exploratory research (multi-variates) and a questionnaire survey (N=1064) to initially construct a team playability model, which was validated by structural equation modeling (SEM). The results show that TR have a significant positive effect on teamwork efficiency, and captains with transformational leadership (TL) styles not only enhance TR but also further improve overall team effectiveness (TE) and player satisfaction. This study provides MMOG developers with theoretical support for designing game mechanics centered on team interaction to enhance overall playability and player stickiness.
Volume: 15
Issue: 2
Page: 644-654
Publish at: 2026-06-01

Can machines imagine? Critical thinking and cultural reasoning in multimodal-multilingual AI

10.11591/ijict.v15i2.pp823-838
Mohammad Awad AlAfnan , Siti Fatimah MohdZuki , Shefa Mohammad AlAfnan
Effective communication across languages and cultures is essential in today’s interconnected world. Multimodal-multilingual language models (MMMLMs) aim to advance this goal by integrating text, speech, and visual understanding across diverse linguistic contexts. This study evaluates four leading MMMLMs-GIT, mPLUG, CLIP, and Whisper + GPT-4V-on cross lingual and cross-modal tasks, including image captioning, visual question answering, speech-to-image generation, and idiomatic translation. Performance was assessed in high-resource (English, Arabic), medium resource (Malay), and low-resource (Macedonian) settings. Results show strong performance in structured tasks but notable limitations in cultural reasoning, figurative language interpretation, and semantic grounding in low-resource environments. GIT delivered the most consistent multilingual results, while Whisper + GPT-4V excelled in fluency yet lacked cultural sensitivity. To address these gaps, the study proposes culturally informed evaluation protocols that integrate quantitative metrics such as BLEU, CIDEr, and F1 with qualitative, community-centered approaches. These include cross-cultural annotation panels, inter-rater reliability validation using Cohen’s kappa, and a novel “cultural fidelity” metric to measure alignment with culturally specific norms. The findings emphasize the need for inclusive datasets, ethical development, and interdisciplinary collaboration to ensure MMMLMs support equitable and culturally aware global communication.
Volume: 15
Issue: 2
Page: 823-838
Publish at: 2026-06-01

Advanced IoT-integrated real-time fire detection and automated mitigation system

10.11591/ijict.v15i2.pp861-868
Rama Krishna Peddarapu , Ajimera Abhinav , Gnana Sathwika V. N. V. , Poosa Brijesh , Amrutha Varshini Ravula
In the field of industry and commerce safety, tackling the most challenging and ongoing fire threats requires the advance internet of things (IoT) integrated real-time fire detection and automated mitigation system. Leveraging IoT and multi-modal sensing in fire safety, the system combines flame, gas, and humidity sensors and cameras to provide continuous real time monitoring and appropriate management of the threats. Real-time automated hazard interventions, such as sprinkler system engagement and geocoded alerts to fire departments, significantly improve life safety outcomes of the system. Active damage mitigation IoT devices provide integrated damage mitigation safety and individual IoT device remote monitoring. In the scope of industry and commerce, this system is a demonstration of the impact of IoT on improving fire safety.
Volume: 15
Issue: 2
Page: 861-868
Publish at: 2026-06-01
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