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International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 552 Documents
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When web apps heal themselves: a MAPE-K based approach to fault tolerance and adaptive recovery G. Aribe Jr., Sales; G. Oracion, Rov Japheth
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp729-740

Abstract

Ensuring the reliability and resilience of modern web applications remains a critical challenge due to increasing system complexity and dynamic runtime environments. This study proposes a modular self-healing framework based on the monitor–analyze–plan–execute over a shared knowledge base (MAPE-K) model, integrated with an AutoFix-inspired mechanism for adaptive fault recovery. Using a design and development research (DDR) approach, the system was implemented and evaluated through controlled fault injection experiments across twenty runtime failure scenarios, including service crashes, memory leaks, and database disconnections. Experimental results demonstrate that the proposed framework achieved a mean fault detection F1-score of 90.7% and a recovery success rate of 93.2%. The AutoFix module reduced the average time-to-recovery (TTR) by 56.2%, achieving an average recovery time of 3.92 seconds. System throughput was maintained between 88% and 95% during fault conditions, with only a 3.1% increase in response time. Additionally, iterative feedback mechanisms improved recovery efficiency by 18.6% over multiple cycles. These findings indicate that the proposed framework provides a practical and extensible approach to enhancing fault tolerance in web applications through feedback-driven adaptation. While the current implementation relies on predefined recovery strategies, the integration of learning-oriented feedback establishes a foundation for future development of more autonomous self healing systems.
Energy-efficient lightweight blockchain framework for scalable and secure sensor networks Swapna Kumar, Surendran; Satyanarayan Reddy, Kalli
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp655-664

Abstract

Wireless sensor networks (WSNs) integrated with the internet of things (IoT) are hybrid technologies of interconnected systems. The IoT connects various devices, from sensors to smart gadget networks, and leverages a framework to provide secure solutions. This paper presents a lightweight adaptive proof-of-stake (APoS) blockchain framework design specifically for IoT-WSN. It focuses on efficient energy, scalability, and robust security. The proposed model integrates a hybrid APoS-delegated PoS (DPoS) consensus mechanism, trust-based routing, and a random forest (RF)-driven intrusion detection system (IDS). Extensive simulations of 100 to 10,000 nodes display energy usage of 0.018–0.019 mJ/node, breach of privacy rates of 0.02%, and throughput up to 9.92 tx/round for 1,000 nodes and 3.40 tx/round for GreenOrbs validation. The IDS achieves 94.21% accuracy for 1,000 nodes and 88.89% for GreenOrbs against distributed denial-of-service (DDoS), Sybil, and Jamming attacks. Validated using the GreenOrbs dataset, the framework ensures real-world applicability in resource-constrained WSNs. Future research has validated and verified the use of APoS and PoS hybrid models for broader decentralised IoT–WSN deployments.
IoT-enabled smart hydroponic system using nutrient film technique for precision agriculture Kumara, Varuna; Naik, Akshatha; Tahsir, Fatima; Bommayya Devadiga, Sinchana; Ramesh Naik, Vinitha
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp900-908

Abstract

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.
Arobust outlier detection based filtering for noise removal in grayscale images Salem Al Rawash, Ali; Aini Abdullah, Farah; Kadri Junoh, Ahmad; Alshbeel, Abdallah; Banikhalid, Mohammed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp508-522

Abstract

Salt-and-pepper noise severely degrades the visual quality of digital images, par ticularly at high noise densities where conventional denoising techniques often fail. Median- and mean-based filters tend to oversmooth images and blur fine structures when the majority of pixels within a local window are corrupted. This paper proposes a robust dual-layer denoising framework for grayscale images that integrates rank-based prescreening, interquartile range (IQR)-based statis tical outlier detection using Tukey fences, and a lightweight post-processing sharpening stage. In the first layer, a rank-4 trimmed estimator suppresses ex treme impulse values and stabilizes local statistics. In the second layer, adap tive IQR thresholds are employed to detect and replace residual outliers, even in heavily corrupted neighborhoods. A final step involving selective sharpen ing combined with mild smoothing enhances edge details without amplifying residual noise. Extensive experiments on standard grayscale images (Lenna, Barbara, lake, boat, and living room) across salt-and-pepper noise levels from 10% to 90% demonstrate that the proposed approach consistently outperforms conventional methods, including mean, median, Gaussian, modified decision based unsymmetrical trimmed median filter (MDBUTMF), and pixel density based filter (BPDF). Quantitative evaluation indicates peak signal-to-noise ratio (PSNR) values reaching 38.23dB, structural similarity index (SSIM) values up to 0.99, and significant reductions in mean squared error (MSE), particularly at higher noise densities. These results confirm that the proposed framework ef fectively suppresses noise while preserving edges and textures, making it well suited for practical applications such as medical imaging, remote sensing, and surveillance.
Enhancing road damage detection performance using the YOLOv9 model Farkhan Adhitama, Muhammad; Sutikno, Sutikno; Rismiyati, Rismiyati
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp616-624

Abstract

Roads are essential infrastructure that support community mobility, and their condition significantly impacts road user safety. However, manual road damage detection remains inefficient, time-consuming, costly, and prone to human error. To address this issue, this study proposed the YOLOv9 model for automated road damage detection and explored parameter combinations to optimize its performance. The proposed solution leverages the YOLOv9 model, which offers enhanced detection speed and accuracy compared to previous YOLO versions, due to its improved backbone and dynamic label assignment techniques. The method uses pre-trained weights and performs parameter tuning to adapt the model for identifying common road defects, including potholes, longitudinal, lateral, and alligator cracks. A publicly available dataset of road condition images was used for training and evaluation. Experimental results demonstrated that the optimized YOLOv9 model achieved a mean average precision (mAP) of 62.8%, indicating a promising ability to detect multiple types of road damage accurately. This study highlights the potential of YOLOv9 as an effective tool for road monitoring systems, contributing to proactive maintenance strategies and more efficient infrastructure management.
Harnessing NLP and AI to decode political discourse: speech patterns, sentiment analysis, and public perception Kumar, Malayaj; Kumar Singh, Anuj; Das, Soumitra
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp674-682

Abstract

Using natural language processing (NLP) and artificial intelligence (AI), this study analyzes the frequencies of words and phrases in political leaders’ speeches to track patterns in political discourse. The objective is to identify language patterns, sentiments, and topics of political addresses using state of-the-art methods like automatic transcription (Whisper), Bidirectional gated recurrent unit (GRU) for sentiment analysis, and BERTopic. Through the use of Whisper’s state-of-the-art transcription service, we were able to transcribe the political speeches into machine-readable text, which in turn provides for other types of analysis. Bidirectional GRU classifies sentiment as positive, negative, or neutral with the aim to study how politicians use sentiment to manipulate their listeners. Furthermore, we use BERTopic for tracking the evolution of rhetoric, key trend summarisation, and topic mining and analysis. It illustrates how politicians employ discursive strategies and epilinguistic elements to manage the public mind and reality. Achievements and objectives are framed with positive and defensive emotions aimed at threats or criticisms. The emotional grab of it all is still important. It locates in these the thematic coherence and shifting sentiment that lie at the heart of political storytelling. It shows how political communication is evolving to stay relevant in the digital media age and delivers language – even real-time language pattern tracking – via the use of AI and big data. Further study is needed of multimodal and flexible techniques for analysing political discourse across languages and time periods.
Ensemble windows intrusion detection system using XGBoost and deep learning Kedambady Shiva, Pranitha; D. Shetty, Pushparaj
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp565-577

Abstract

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.
A critical review of information retrieval techniques: current trends and challenges D. Patil, Sanket; Aalam, Zahir
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp456-464

Abstract

The realm of information retrieval is witnessing transformative advancements, driven by the integration of deep learning techniques, specialized algorithms, and domain-specific applications. Information retrieval systems play an important role in many applications including in the Artificial Intelligence powered systems that can be seen in many applications. Information Retrieval, generally, acts an important task in the knowledge discovery phase of any query based intelligent system. This paper presents a comprehensive review by conducting a detailed analysis of the technological nuances, dataset specifications, and pivotal findings. This detailed review has been done with the special emphasis on the kind of technology used to achieve accurate information retrieval, domain of the study, and the system’s ability to retain or work with tables and figures, among other parameters. Navigating through the rich tapestry of methodologies, the paper underscores the pivotal role of deep learning frameworks in revolutionizing traditional retrieval paradigms. Furthermore, it sheds light on the innovative integration of textual information, algorithmic advancements, and specialized datasets to enhance the efficacy and granularity of information retrieval mechanisms.
A new modified characteristic equation for optimal coordination of directional overcurrent relays Rao Battu, Neelakanteshwar; Reddy Salkuti, Surender
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp789-796

Abstract

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.
Manufacturing mycelium moulds under controlled conditions using IoT V., Subbulakshmi; Katiravan, Jeevaa; M., Parvathy; S., Sridevi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp880-890

Abstract

In the process of making plastics, potentially dangerous substances like colourants or stabilisers are added. One example is phthalates, which are used to make PVC. The ecology is significantly impacted by the way plastic products are disposed of as well. The majority of plastics can take a long time to biodegrade lengthy time to break down if disposed of in a landfill. The issue of plastic trash is getting worse. Plastic is incredibly valuable due to its cheap availability and low cost of production; however, its recyclability has been oversold. Mycelium mould is a fantastic substitute for plastic. Mycelium is more efficient in terms of biodegradability and sustainability compared to plastic. The properties of Mycelium include heat insulation, fire resistance, water resistant, acoustic insulation, low weight, vegan meat, beauty products, and mainly bio-degradable. All these features make mycelium our only last chance to win the war against the plastic with greater potential than the other alternatives for plastics available in the market currently. Here, we have shown how mycelium can be grown in the most efficient way ever without any contamination and faster growth cycle. The primary goal is to lower the cost of mycelium mould, lessen mycelium spoiling, and accelerate its growth cycle by offering an ideal growing.

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