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A unified intelligent AI platform for resolving citizens' queries related to beneficiary service using AI -Powered chatbots a practical apparoach

10.11591/ijict.v15i1.pp267-275
Parveen Mehta , Shweta Bansal
The daily many rural citizens visit government offices to inquire about beneficiary services that support poor and illiterate citizens. However, without proper knowledge, many eligible citizens fail to benefit from these services. In the artificial intelligence (AI) era, AI-powered chatbots, such as AI agents, can provide valuable support to the villagers and provide them with complete information at their door step. In this paper, a proposed framework, using a chatbot, to reduce the communication gap between citizens and government officials to improve service delivery performance. This chatbot is developed by using a built large language model, python libraries, fast API, and mongodb data base. Our findings demonstrate the challenges of imbalanced data and suggest improvements for future implementations. The system enhances service delivery by automating eligibility checks and reducing office visit frequency by up to 60%.
Volume: 15
Issue: 1
Page: 267-275
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

Fetal electrocardiogram extraction and signal quality assessment using statistical method

10.11591/ijict.v15i1.pp217-227
Li Mun Ng , Nur Anida Jumadi , Farah Najidah Noorizan
Abdominal electrocardiogram (aECG) can be used to monitor fetal heart rate (fHR), providing critical insights into fetal health during pregnancy. However, separating the mixed signals of fetal ECG (fECG) and maternal ECG (mECG) within the aECG remains a critical challenge. This paper investigates the integration of statistical metrics, including signal-to-noise ratio (SNR), skewness, kurtosis, standard deviation, and variance to assess fECG signal quality during extraction using three adaptive filtering metods ((Least mean square (LMS), normalized LMS (NLMS), and recursive least square (RLS)) and independent component analysis (ICA). The findings reveal that RLS achieves the best performance among the three AF methods, with the highest SNR of 5.6 dB at the step size, µ of 0.9. For ICA with a bandpass Chebyshev filter (low-cut frequency = 1 Hz, high-cut frequency = 50 Hz) produces an SNR of 0.86 dB. Additionally, both RLS and ICA yield similar fHR values of 133 bpm with a PE measurement of 0.9%. In conclusion, integrating statistical metrics with ICA and RLS effectively extracts fECG with good signal quality. Future research could explore other ECG datasets and incorporate machine learning to further improve fECG extraction and signal quality assessment.
Volume: 15
Issue: 1
Page: 217-227
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

Renewable energy optimization for sustainable power generation

10.11591/ijict.v15i1.pp365-373
Debani Prasad Mishra , Sarita Samal , Rohit Kumar , Arun Kumar Sahoo , Surender Reddy Salkuti
To improve sustainability in power generation, this study presents a thorough data-driven method for maximizing renewable energy sources. It employs measures like capacity utilization factor (CUF) and efficiency to evaluate the performance of solar and wind energy using historical weather and energy-generating data. The study offers practical suggestions for improving renewable energy systems, such as weather-energy correlation analysis and machine learning-based forecasting models. In addition, a comparative analysis is carried out to ascertain which energy source is better, and useful real-world data is provided, including a summary of all India’s total renewable energy generation (excluding large hydro) for June 2023 and a performance comparison year over year. A useful, data-driven approach for enhancing renewable energy is provided by this work, which advances the topic of sustainable energy.
Volume: 15
Issue: 1
Page: 365-373
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

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

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

Development and performance evaluation of a CNN model for seagrass species classification in Bintan, Indonesia

10.11591/csit.v7i1.p20-29
Nurul Hayaty , Hollanda Arief Kusuma
This study presents the development and evaluation of a convolutional neural network (CNN) model for automated seagrass species classification in Bintan, Indonesia. The objective of this research is to examine how different train-validation data split ratios affect model accuracy and generalization performance. The CNN was trained under four configurations (60:40, 70:30, 80:20, and 90:10) to analyze the influence of training data volume on learning convergence and predictive capability. The results indicate that all configurations achieved high validation accuracy, with the best performance reaching 98.53% when using the 90:10 split. Evaluation on unseen data demonstrated that the 60:40 configuration provided the most consistent and reliable generalization. Performance variations were also affected by the morphological similarity between the classified species, which increases the challenge in correctly distinguishing certain classes. Overall, the findings confirm the effectiveness of CNN-based classification for supporting marine biodiversity monitoring and underline the importance of dataset composition in achieving optimal performance. Future improvements will focus on expanding data variability to enhance robustness in real-world scenarios.
Volume: 7
Issue: 1
Page: 20-29
Publish at: 2026-03-01

Review on patch antenna for 5G Networks at Ka-Band

10.11591/csit.v7i1.p102-110
Md. Nurullah Al Nasib , Md. Sohel Rana
Microstrip antennas for Ka-band wireless applications will be thoroughly examined in this research. To utilize 5G wireless applications, a new research topic that has been established is the creation of microstrip patch antennas. Patch antennae are made of different shapes, such as rectangles, circular shapes, triangles, donuts, rings, etc. Many substrate materials are used in patch antenna designs. This article examines the geometric configurations of antennas, the many methods of analysis for attributes of antennas, the dimensions of antennas, the issues that antennas face, and the potential solutions to those challenges. Wireless communication technologies, such as television broadcasts, microwave ovens, mobile phones, wireless local area networks (LANs), Bluetooth, global positioning systems (GPS), and two-way radios, all use it. This article examines the geometric structures of antennas, including several characteristics and materials by which they are constructed, as well as the numerous shapes they can produce. This paper will also examine return loss (S11), bandwidth, voltage standing wave ratio (VSWR), gain, directivity, efficiency, and Bandwidth discussed in the prior studies. In the future, a novel patch antenna can be designed for 5G wireless applications.
Volume: 7
Issue: 1
Page: 102-110
Publish at: 2026-03-01

Deep learning for sentiment analysis and topic extraction in health insurance

10.11591/csit.v7i1.p66-73
Muzondiwa Karomo , Mainford Mutandavari , Wilton Muzava
Social media has transformed into a vital channel for real-time, unsolicited feedback in healthcare, yet health insurance providers often lack the tools to mine insights from such data. This study proposes a cloud-based system leveraging deep learning for sentiment analysis and topic modeling tailored to the Commercial and Industrial Medical Aid Society (CIMAS) health insurance in Zimbabwe. Using bidirectional encoder representations from transformers (BERT), a convolutional neural network (CNN), a random forest (RF), and autoencoders, the system processes multilingual data from platforms like Twitter and Facebook, identifying customer concerns in real time. Over 15,000 posts were analyzed, with CNN achieving 91.4% accuracy in sentiment classification and BERTopic extracting coherent themes. The system detected issues such as claim delays, app navigation problems, and unreported anomalies. Findings demonstrate that AI can improve service delivery, customer satisfaction, and responsiveness in African insurance contexts.
Volume: 7
Issue: 1
Page: 66-73
Publish at: 2026-03-01

Digital platforms and cloud computing for smart cities: a review

10.11591/ijict.v15i1.pp30-38
William Christopher Immanuel , Anitha Juliette Albert , Limsa Joshi Jerald Jobitham , Roselene Rebecca Selvaraj , Benita Sharon Ruban , Bennet Vini Robin , Andria Morais Allen
The rapid urbanization of the modern world initiated the emergence of digital cities, where advanced technologies converge to optimize urban living and address the limitations of a rapidly growing population. Central to this transformation are digital platforms and cloud computing. These interconnected technologies aid in shaping the future of urban landscapes, fostering sustainability, efficiency, and improved quality of life. Digital platforms serve as the backbone of smart cities, enabling seamless integration and management of various urban services and systems. One significant application of digital platforms in smart cities is the implementation of intelligent transportation systems (ITS). By integrating real-time traffic data, public transit information, and ride-sharing services, these platforms facilitate efficient transportation management, reduce congestion, and decrease carbon emissions. Cloud computing serves as a key enabler for managing the massive data flows generated by smart city infrastructures. The scalability and flexibility offered by cloud-based solutions allow cities to manage their resources efficiently and access computing power on demand without the need for extensive physical infrastructure. Cloud computing enhances smart city development by enabling collaborative data access and interaction among diverse stakeholders, from government agencies to private firms and residents.
Volume: 15
Issue: 1
Page: 30-38
Publish at: 2026-03-01

Students performance clustering for future personalized in learning virtual reality

10.11591/ijece.v16i1.pp297-310
Ghalia Mdaghri Alaoui , Abdelhamid Zouhair , Ilhame Khabbachi
This study investigates five clustering algorithms—K-Means, Gaussian mixture model (GMM), hierarchical clustering (HC), k-medoids, and spectral clustering—applied to student performance in mathematics, reading, and writing to support the development of virtual reality (VR)-based adaptive learning systems. Cluster quality was assessed using Davies-Bouldin and Calinski-Harabasz indices. Spectral clustering achieved the best results (DBI = 0.75, CHI = 1322), followed by K-Means (DBI = 0.79, CHI = 1398), while HC demonstrated superior robustness to outliers. Three distinct student profiles—beginner, intermediate, and advanced—emerged, enabling targeted adaptive interventions. Supervised classifiers trained on these clusters reached up to 99% accuracy (logistic regression) and 97.5% (support vector machine (SVM)), validating the discovered groupings. This work introduces a novel, data-driven methodology integrating unsupervised clustering with supervised prediction, providing a practical framework for designing immersive VR learning environments.
Volume: 16
Issue: 1
Page: 297-310
Publish at: 2026-02-01

Accessibility in e-government portals: a systematic mapping study

10.11591/ijece.v16i1.pp357-372
Mohammed Rida Ouaziz , Laila Cheikhi , Ali Idri , Alain Abran
In recent years, several researchers have investigated the challenges of accessibility in e-government portals and have contributed to many proposals for improvements. However, no comprehensive review has been conducted on this topic. This study aimed to survey and synthesize the published work on the accessibility of e-government portals for people with disabilities. We carried out a review using a systematic mapping study (SMS) to compile previous findings and provide comprehensive state-of-the-art. The SMS collected studies published between January 2000 and March 2025 were identified using an automated search in five known databases. In total, 112 primary studies were selected. The results showed a notable increase in interest and research activities related to accessibility in e-government portals. Journals are the most widely used publication channel; studies have mainly focused on evaluation research and show a commitment to inclusivity. “AChecker” and “Wave validator” are the most used accessibility evaluation tools. The findings also identified various accessibility guidelines, with the most frequently referenced being the web content accessibility guidelines (WCAG). Based on this study, several key implications emerge for researchers, and addressing them would be beneficial for researchers to advance e-government website accessibility in a meaningful way.
Volume: 16
Issue: 1
Page: 357-372
Publish at: 2026-02-01

An information retrieval system for Indian legal documents

10.11591/ijece.v16i1.pp246-255
Rasmi Rani Dhala , A V S Pavan Kumar , Soumya Priyadarsini Panda
In this work, a legal document retrieval system is presented that estimates the significance of the user queries to appropriate legal sub-domains and extracts the key documents containing required information quickly. In order to develop such a system, a document repository is prepared comprising the documents and case study reports of different Indian legal matters of last five years. A legal sub-domain classification technique using deep neural network (DNN) model is used to obtain the relevance of the user queries with respective legal sub-domains for quick information retrieval. A query-document relevance (QDR) score-based technique is presented to rank the output documents in relation to the query terms. The presented model is evaluated by performing several experiments under different context and the performance of the presented model is analyzed. The presented model achieves an average precision score of 0.98 and recall score of 0.97 in the experiments performed. The retrieval model is assessed with other retrieval models and the presented model achieves 13% and 12% increase average accuracy with respect to precision scores and recall measures respectively compared to the traditional models showing the strength of the presented model.
Volume: 16
Issue: 1
Page: 246-255
Publish at: 2026-02-01
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