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29,061 Article Results

A decision support system for mushroom classification using Naïve Bayesian algorithm

10.11591/ijict.v15i1.pp138-151
Vilchor G. Perdido , Thelma D. Palaoag
Mushrooms are rich in vitamins and proteins, a well-known superfood, however, cases of harmful mushroom consumption worldwide result in hallucinations, illness, or death. A significant challenge is that some poisonous mushrooms closely resemble edible varieties, making it difficult for mushroom foragers to distinguish between them. This study introduced KabuTeach, a decision support system (DSS) designed to classify mushrooms based on their morphological characteristics using the Naïve Bayes (NB) algorithm. The classification model was applied to a real-world dataset of 8,124 instances from Kaggle, containing 23 attributes. Evaluation metrics, including accuracy, recall, precision, specificity, and F1-score, were used to assess the classifier’s performance. Results indicated that the NB classification algorithm integrated into KabuTeach achieved a high accuracy level of 89.13%, using a 70:30 data split and 5-fold cross-validation approaches. The 0.98 AUC (area under the curve) value further concluded that the model was excellent in classifying between edible and poisonous mushrooms. These findings showed that KabuTeach is a reliable classification tool that aids mushroom foragers in differentiating mushrooms and promoting safer consumption practices. This innovation in agricultural technology could potentially reduce health risks by minimizing accidental ingestion of toxic mushrooms, ultimately contributing to public health safety.
Volume: 15
Issue: 1
Page: 138-151
Publish at: 2026-03-01

Classification and regression tree model for diabetes prediction

10.11591/ijict.v15i1.pp207-216
Farah Najidah Noorizan , Nur Anida Jumadi , Li Mun Ng
Diabetes mellitus is characterized by excessive blood glucose that occurs when the pancreas malfunctions while producing insulin. High blood glucose levels can cause chronic damage to organs, particularly the eyes and kidneys. Diabetes prediction models traditionally use a variety of machine learning (ML) algorithms by combining data from the glucose levels, patient health parameters, and other biomarkers. Prior research on diabetes prediction using various algorithms, such as support vector machine (SVM) and decision tree (DT) models, demonstrates an accuracy rate of approximately 70%, which is relatively modest. Therefore, in this study, a classification and regression tree (CART) multiclassifier model has been proposed to improve the accuracy of diabetes prediction, which is based on three classes: non-diabetic, pre-diabetic, and diabetic. The study involved data preprocessing steps, hyperparameter tuning, and evaluation of performance metrics. The model achieved 97% accuracy while utilizing the value of 5 for the number of leaves per node, the value of 10 for the maximum number of splits, and deviance as the split criterion, which also resulted in a precision of 98%, recall of 97%, and F1-score of 98%, showing that the proposed multiclassifier model can accurately predict diabetes. In conclusion, the proposed CART model with the best hyperparameter setting can enable the highest accuracy in predicting diabetes classes.
Volume: 15
Issue: 1
Page: 207-216
Publish at: 2026-03-01

Practice-based teaching using an AI platform to strengthen faculty competency

10.11591/ijict.v15i1.pp171-178
Angsana Phonsuk , Phakharach Plirdpring
This research aimed to i) analyze faculty members’ knowledge, understanding, and skills in using AI for practice-based teaching enhancement, ii) evaluate factors affecting faculty readiness in integrating AI into teaching processes, and iii) design and develop an AI platform to enhance faculty competency in practice-based teaching. The questionnaire, validated by five experts, was administered to 200 respondents divided into two groups: 100 faculty members from public universities and 100 from private universities. Comparative analysis revealed that public university faculty and private university faculty statistically significant differences in challenges and concerns at the 05 level, with public university faculty expressing higher concerns. Significant differences were found in AI experience and skills, attitudes toward AI use, and challenges and concerns. However, no significant differences were observed in three other areas: AI knowledge and understanding, AI readiness, and belief in AI’s effectiveness for practice-based learning enhancement. Data from both groups were utilized in designing and developing the AI platform to enhance practicebased teaching competency in higher education. Expert evaluation of the platform’s suitability showed high levels of demand for the AI platform and high appropriateness of the technology used in platform development.
Volume: 15
Issue: 1
Page: 171-178
Publish at: 2026-03-01

Fuzzy logic-based driver fatigue prediction system for safe and eco-friendly driving

10.11591/ijict.v15i1.pp84-92
Raghavan Sheeja , Chidambaranathan Bibin , Selvaraj Vanaja , Shakeela Joy Arul Dhas , Alex Arockia Abins , Padmavathi Balasubramaniam
The advancement of intelligent car systems in recent years has been significantly influenced by developments in information technology. Driver fatigue is a dominant problem in car accidents. The goal of advanced driving assistance is to develop an advanced driving assistance system (ADAS) a eco-friendly model which focuses on the detection of drowsy driver, to notify drivers of their fatigued condition to prevent accidents on the roads. With relation to driving, the driver mustn’t be distracted by alarms when they are not tired. The answer to this unanswered question is provided by 60- second photograph sequences that were taken when the subject’s face was visible. To reduce false positives, two alternative solutions for determining whether the driver is drowsy have been developed. To extract numerical data from photos and feed it into a fuzzy logic-based system, convolutional network is applied initially; later deep learning technique is followed. The fuzzy logic-based solution avoids the false alarm of the system.
Volume: 15
Issue: 1
Page: 84-92
Publish at: 2026-03-01

Reputation-enhanced two-way hybrid algorithm for detecting attacks in WSN

10.11591/ijict.v15i1.pp428-437
Divya Bharathi Selvaraj , Veni Sundaram
Wireless sensor networks (WSNs) are susceptible to a variety of attacks, such as data tampering attacks, blackhole attacks, and grayhole attacks, that can affect the reliability of communication. We proposed a reputationenhanced two-way hybrid algorithm (RCHA) that uses cryptographic hash functions and reputation-based trust management to detect and de-escalate attacks accurately. The RCHA algorithm implements two hash functions RACE integrity primitives’ evaluation message digest (RIPEMD) and secure hash algorithm (SHA-3), to initiate the integrity check for the entire packet sent across the network. Every node in the WSN tracks a reputation score for each neighbor the node is connected to, and this score is dynamically updated based on the behavior of each neighbor. If a neighboring node’s reputation drops below a threshold, the node is sent a maliciousness designation. At that time, the node will broadcast an alert message to its neighboring nodes and begin to reroute its data through one of its trusted neighbors to ensure the reliability of the communication. The simulation results reported that the RCHA algorithm improved the accuracy of the attack detection rate and the number of packets delivered compared to traditional attack detection methods. The RCHA algorithm was able to maintain low computational and energy overhead for the WSN, making it an attractive option for a resource-constrained application in a WSN. Given the trends towards more collaborative networks, the reputation mechanism in the RCHA algorithm improves the overall reliability and capabilities of the WSN, regardless of adversaries.
Volume: 15
Issue: 1
Page: 428-437
Publish at: 2026-03-01

Exploring diverse perspectives: enhancing black box testing through machine learning techniques

10.11591/ijict.v15i1.pp238-246
Heba Nafez Jalal , Aysh Alhroob , Ameen Shaheen , Wael Alzyadat
Black box testing plays a crucial role in software development, ensuring system reliability and functionality. However, its effectiveness is often hindered by the sheer volume and complexity of big data, making it difficult to prioritize critical test cases efficiently. Traditional testing methods struggle with scalability, leading to excessive resource consumption and prolonged testing cycles. This study presents an AI-driven test case prioritization (TCP) approach, integrating decision trees and genetic algorithms (GA) to optimize selection, eliminate redundancy, and enhance computational efficiency. Experimental results demonstrate a 96% accuracy rate and a 90% success rate in identifying relevant test cases, significantly improving testing efficiency. These findings contribute to advancing automated software testing methodologies, offering a scalable and efficient solution for handling large-scale, data-intensive testing environments.
Volume: 15
Issue: 1
Page: 238-246
Publish at: 2026-03-01

A comparative study and design investigation: scalable magnitude comparators across technology nodes

10.11591/ijict.v15i1.pp13-20
Anitha Juliette Albert , Umamaheswari Ramalingam , Ashlin Leon A. S. , Sinthia Panneer Selvam , Sripriya Thiagarajan , Arunkumar Kuppusamy
In recent times, the convergence of innovative design technologies such as very large-scale integration (VLSI), cadence design systems, and fieldprogrammable gate array (FPGA) has become crucial to address the growing demand for enhanced efficiency, scalability, and reduced power consumption in electronic designs. This paper introduces a novel approach to designing non-pipelined and pipelined scalable magnitude comparators (MCs), which integrates 4-bit MCs. The frontend implementation of the MCs is achieved using quartus prime, an FPGA board. The backend implementation is done using cadence design system, evaluated across the three distinct CMOS technology nodes. The literature review highlights the influence of technology scaling on area, power consumption, and propagation delay, analyzing various comparator designs and their associated trade-offs. The results provide valuable insights into the design and optimization of MCs for future applications in image processing and nano computing.
Volume: 15
Issue: 1
Page: 13-20
Publish at: 2026-03-01

Securing Defi: a comprehensive review of ML approaches for detecting smart contract vulnerabilities and threats

10.11591/ijict.v15i1.pp438-446
Dhivyalakshmi Venkatraman , Manikandan Kuppusamy
The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.
Volume: 15
Issue: 1
Page: 438-446
Publish at: 2026-03-01

Dynamic monitoring for enhancing QoS and security in distributed systems

10.11591/ijict.v15i1.pp313-322
Sudhakar Periyasamy , Vijayalakshmi Alagarsamy , Palani Latha , Karuppiah Tamilarasi , Thenmozhi Elumalai , Prabu Kaliyaperumal
Distributed systems are integral to modern digital infrastructure, supporting communication and data exchange across various sectors. Ensuring security while maintaining quality of service (QoS) in such environments presents a significant challenge. This study introduces a dynamic network monitoring system (DNMS) that incorporates adaptive monitoring mechanisms and dynamic security metrics to safeguard distributed systems. The proposed architecture utilizes an event analyzer (EA) to evaluate and classify system events based on criticality, enabling secure transmission decisions and efficient threat detection. Experimental evaluations demonstrate the DNMS achieves a low processing overhead of 12%, supports a high data handling capacity of 5,000 requests per second, and maintains a latency of just 150 milliseconds. Additionally, it ensures strong compliance with regulatory standards-achieving 95% alignment with GDPR and 97% with ISO 27001- and high threat detection accuracy, with 98% for phishing, 94% for malware, and 96% for insider threats. These results confirm the framework’s effectiveness in enhancing adaptive security, offering scalable and regulation-compliant solutions for complex distributed environments.
Volume: 15
Issue: 1
Page: 313-322
Publish at: 2026-03-01

Electrifying the roads using wireless charging solutions for next-gen electric vehicles

10.11591/ijict.v15i1.pp102-110
Venkatesh Kumar Chandrasekaran , Ramesh Babu Muthu , Lekha Shri Sasidar , Malathi Mani
This paper outlines a solar charging device designed for electric vehicles (Evs), mitigating the drawbacks of conventional fuel-based transportation and environmental pollution. Because EVs are becoming more and more popular throughout the world, there are more of them on the road. Beyond environmental benefits, EVs offer cost savings by substituting expensive fuel with more economical electricity. The study introduces innovative solutions in EV charging, enabling separate charging stations, continuous motion charging, and wireless charging without external power sources. The communication and system operations are controlled by an ESP8266 controller. This advanced approach eliminates the need for intermittent charging stops, representing a solar-powered wireless charging solution for plug-in EVs in transit. This work underscores the critical importance of addressing energy and environmental sustainability.
Volume: 15
Issue: 1
Page: 102-110
Publish at: 2026-03-01

GSM based load monitoring system with ADL classification and smart meter design

10.11591/ijict.v15i1.pp74-83
Debani Prasad Mishra , Rudranarayan Senapati , Rohit Kumar Swain , Subhankar Dash , Raj Alpha Swain , Surender Reddy Salkuti
This paper introduces a method for the classification of activities of daily living (ADL) by utilizing smart meter and smart switch data in a synergistic approach. Through the integration of these internet of things (IoT) devices, the paper aims to enhance the application of ADL classification. Guided by recent advancements in load monitoring and energy management systems, the methodology incorporates machine learning techniques to analyze data streams from both the smart meter and smart switch. Drawing inspiration from prepaid smart meter monitoring systems, IoT-based smart energy meters for optimizing energy usage, and energy metering chips with adaptable computing engines, our design incorporates diverse perspectives. Additionally, we consider the utilization of mobile communication for prepaid meters, remote detection of malfunctioning smart meters, and an empirical investigation into the acceptance of IoT-based smart meters. We substantiate our proposed approach through experimental results, showcasing its effectiveness in accurately classifying diverse ADL scenarios. This research contributes to the field of smart home technology by offering an advanced method for ADL classification. The integration of smart meter and smart switch data provides a comprehensive understanding of energy consumption patterns, opening avenues for improved energy management and informed decision-making within smart homes.
Volume: 15
Issue: 1
Page: 74-83
Publish at: 2026-03-01

Enhancing intellectual property rights management through blockchain integration

10.11591/ijict.v15i1.pp111-119
Raghavan Sheeja , Sherwin Richard R. , Shreenidhi Kovai Sivabalan , Srinivas Madhavan
The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.
Volume: 15
Issue: 1
Page: 111-119
Publish at: 2026-03-01

Privacy-preserving fitness recommendation system using modified seagull monarch butterfly optimized deep learning model

10.11591/ijict.v15i1.pp393-404
Esmita Gupta , Shilpa Shinde
This paper presents a novel modified seagull monarch butterfly optimization (MSMBO) algorithm, with a multi-objective focus on privacy and personalization in the fitness recommender system using a refined three-tier deep learning structure. The method is divided into three phases. In the first phase, fitness data from wearable devices undergoes preprocessing to eliminate noise and standardize features. The second phase incorporates improved elliptic curve cryptography (IECC) alongside the MSMBO to encrypt user data securely, ensuring privacy in cloud storage. This phase also enhances neural network performance by optimizing weights and hyperparameters through feature selection, effectively reducing data complexity while boosting accuracy. In the third phase, ConvCaps extracts spatial data features, while Bi-LSTM identifies temporal dependencies. The proposed system balances multiple objectives like novelty, accuracy, and precision, while safeguarding user data through robust encryption. With the experimental findings, our suggested method performs better than current existing models, especially in heart rate prediction and fitness pattern identification. The overall outcome makes the system ideal for privacyconscious, personalized fitness recommendations. The model’s shows significant improvement in mean squared error (MSE), normalized mean squared error (NMSE), and mean absolute percentage error (MAPE), thus verifying its effectiveness in secure, real-time fitness tracking.
Volume: 15
Issue: 1
Page: 393-404
Publish at: 2026-03-01

Applying fuzzy Tsukamoto method to improve production efficiency in manufacturing industry

10.11591/ijict.v15i1.pp356-364
Moch Taufik , Andi Riansyah , Muhammad Qomaruddin , Muhammad Sholahuddin
Manufacturing can increase competitiveness and reduce costs by improving production efficiency. The study’s goal is to develop a production prediction system using the fuzzy Tsukamoto technique. This method is used to model the uncertainty that occurs during the production process. Thus, production planning based on demand and inventory availability can be more accurate. After being tested on production data from a manufacturing company, the fuzzy Tsukamoto method showed the ability to make more efficient decisions than conventional methods. This system not only significantly reduces production costs but also improves overall operational efficiency, including resource management, waste reduction, and cycle time optimization. The adoption of this method provides added value to companies in facing increasing market competition while keeping production costs low without compromising quality.
Volume: 15
Issue: 1
Page: 356-364
Publish at: 2026-03-01

Design and development of machine learning-based web application for oil palm yield prediction

10.11591/ijict.v15i1.pp228-237
Yuhao Ang , Helmi Shafri , Yang Ping Lee , Shahrul Azman Bakar , Hwee San Lim , Rosni Abdullah , Yusri Yusup , Mohammed Mustafa AL-Habshi
The prediction of crop yields is influenced by various factors such as weather conditions, agronomic practices, and management strategies. Accurately predicting oil palm yield is crucial for sustainable production, as it plays a significant role in global food security. Challenges such as climate change and nutrient deficiencies have adversely affected yields, highlighting the necessity for a specialized web application tailored to the oil palm industry. This study presents a machine-learning-based web application that utilizes a deep learning model to estimate oil palm yields by integrating key parameters, including weather, agronomy, and satellite data. The application features a user-friendly interface and a dashboard for comparing predicted and actual yields, enhancing user engagement and facilitating collaboration among stakeholders. By deploying this tool on the cloud, plantation managers can make informed decisions early in the yield prediction process, ultimately improving plantation management and profitability. This web application is designed to provide valuable insights to stakeholders, contributing to effective decision-making in the oil palm sector.
Volume: 15
Issue: 1
Page: 228-237
Publish at: 2026-03-01
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