International Journal of Informatics and Communication Technology (IJ-ICT)
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.
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<![CDATA[Deep learning algorithms for breast cancer detection from ultrasound scans ]]>
<![CDATA[Lawysen, Lawysen]]>;
<![CDATA[Kusuma, Gede Putra]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp427-437
<![CDATA[Breast cancer is a highly dangerous disease and the leading cause of cancer related deaths among women. Early detection of breast cancer is considered quite challenging but can offer significant benefits, as various treatment interventions can be initiated earlier. The focus of this research is to develop a model to detect breast cancer based on ultrasound results using deep learning algorithms. In the initial stages, several preprocessing processes, including image transformation and image augmentation were performed. Two types of models were developed: utilizing mask files and without using mask files. Two types of models were developed using four deep learning algorithms: residual network (ResNet)-50, VGG16, vision transformer (ViT), and data-efficient image transformer (DeiT). Various algorithms, such as optimization algorithms, loss functions, and hyperparameter tuning algorithms, were employed during the model training process. Accuracy used as the performance metric to measure the model’s effectiveness. The model developed with ResNet-50 became the best model, achieving an accuracy of 94% for the model using mask files. In comparison, the model developed with ResNet-50 and DeiT became the best model for the model without mask files, with an accuracy of 80%. Therefore, it can be concluded that using mask files is crucial for producing the best-performing model.]]>
<![CDATA[An improved approximate parallel prefix adder for high performance computing applications: a comparative analysis ]]>
<![CDATA[Anagani, Vamsidhar]]>;
<![CDATA[Geethanjali, Kasi]]>;
<![CDATA[Gorantla, Anusha]]>;
<![CDATA[Devi, Annamreddy]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp382-392
<![CDATA[Binary adders are fundamental in digital circuit designs, including digital signal processors and microprocessor data path units. Consequently, significant research has focused on improving adders’ power-delay efficiency. The carry tree adder (CTA) is alternatively referred to as the parallel prefix adder (PPA), is among the fastest adders, achieving superior performance in very large scale integrated (VLSI) implementations through efficient concurrent carry generation and propagation. This study introduces approximate PPAs (AxPPAs) by applying approximations in prefix operators (POs). Four types of AxPPAs approximate kogge-stone, approximate brent-kung, approximate ladner fischer, and approximate sparse kogge-stone-were designed and implemented on FPGA with bit widths up to 64-bit. Delay measurements from static timing analysis using Xilinx ISE design suite version 14.7 indicate that AxPPAs exhibit better latency performance than traditional PPAs. The AxPPA sparse kogge-stone, in particular, demonstrated superior area and speed performance, achieving a delay of 2.501ns for a 16-bit addition.]]>
<![CDATA[Advancing semiconductor integration: 3D ICs and Perylene-N as superior liner material for minimizing TSV clamour coupling ]]>
<![CDATA[Dhal, Pradyumna Kumar]]>;
<![CDATA[Rajesh, Murkur]]>;
<![CDATA[Vali, Shaik Hussain]]>;
<![CDATA[Krishna, Sadhu Radha]]>;
<![CDATA[Kumar, Malagonda Siva]]>;
<![CDATA[Rafi, Vempalle]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp605-613
<![CDATA[The semiconductor industry faces substantial challenges with planar integration (2D ICs), prompting a significant shift towards vertical IC integration, known as three-dimensional IC (3D ICs). This deliberate slant not only amplifies bandwidth and boosts system action but also effectively reduces power consumption through scaling. 3D ICs intricately coordinate IC chips by vertically stacking them and establishing electrical connections using through silicon vias (TSVs). TSV clamour coupling emerges as a critical factor influencing system performance, particularly between signalcarrying TSVs (ETSV) and victim TSVs. This study showcases significant advancements in electrical integrity by effectively minimizing clamour coupling from TSVs to the silicon substrate. This is achieved through the application of CMOS-compatible dielectric materials as liner structures. Various proposed structures have been meticulously analyzed across an assortment of parameters, encompassing electrical signals and high frequencies. Moreover, the study rigorously investigates clamour coupling across different types of TSVs, including ETSV, thermal TSV (TTSV), and heat sources. Perylene-N emerges as a standout performer among the tested liner materials, demonstrating superior clamour coupling performance across all proposed models, even at higher frequencies such as THz. In this study a novel dielectric material Perylene-N compared with the conventional SiO2 (silicon dioxide). Notably, Perylene-N exhibited a remarkable 33 dB improvement in noise coupling performance at terahertz (THz) frequencies. The results were thoroughly verified and validated in the research work.]]>
<![CDATA[Bolstering image encryption techniques with blockchain technology - a systematic review ]]>
<![CDATA[Annadurai, Narmadha]]>;
<![CDATA[Ramu, Agusthiyar]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp594-604
<![CDATA[Multimedia data plays a momentous role in present world. With the advancements in various fields of research like internet of things (IoT), industrial IoT (IIoT), cloud computing, medical image processing, and many more technologies, the digital images have already encroached the multimedia eon. The major challenge lies in providing a tamper proof image with higher level of security and confidentiality while being transmitted through a public network. Image encryption techniques are considered to be the predominant method to anticipate security from any unauthorized user access. This has indeed provoked the researchers to create new diverse and hybrid algorithms for encrypting the images. At present blockchain has been the most prevalently discussed method for security and the next level of security can be foreseen using the blockchain encryption techniques. This paper identifies the literature which mainly focuses on assorted image encryption techniques with blockchain technology applied on digital images from heterogeneous sources. An overview has been proposed to discuss on these techniques.]]>
<![CDATA[An IoT-based approach for microclimate surveillance in greenhouse environments ]]>
<![CDATA[Ardiansah, Irfan]]>;
<![CDATA[Nur Perwitasari, Sophia Dwiratna]]>;
<![CDATA[Kastaman, Roni]]>;
<![CDATA[Pujianto, Totok]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp717-727
<![CDATA[As the demand for efficient and cost-effective greenhouse microclimate surveillance has increased, we developed a microclimate surveillance system using microcontroller technology that automatically collects temperature and relative humidity data and transmits it to a cloud server for remote surveillance and data analysis. 1971 microclimate data points were acquired over a 20-day evaluation period, providing insights into greenhouse environmental conditions with a negative linear regression between air temperature and relative humidity within the greenhouse and an R-squared of 0.73. This illustrates the system’s ability to record and quantify environmental conditions data. Additionally, we derived a predictive model to manage microclimate conditions using the regression formula y = -6.12X + 238.33, where X represents the air temperature and y represents the relative humidity. All the results show that the acquired data can be used to make decisions to optimize plant growth. The prototype we developed can be an alternative option for small and medium-sized farms that need a greenhouse surveillance system to improve operational efficiency and reduce surveillance costs. The system can be further developed by implementing additional sensors to monitor light intensity, wind speed, or soil moisture and further data analysis models to optimize greenhouse management.]]>
<![CDATA[Interoperability in healthcare: a critical review of ontology approaches and tools for building prescription frameworks ]]>
<![CDATA[Okon, Eunice Chinatu]]>;
<![CDATA[Sigwele, Tshiamo]]>;
<![CDATA[Galani, Malatsi]]>;
<![CDATA[Mokgetse, Tshepiso]]>;
<![CDATA[Hlomani, Hlomani]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp366-381
<![CDATA[Efficient healthcare interoperability is pivotal for delivering high-quality patient care. This research article presents a critical review of ontology based approaches and tools in the development of ontology-based electronic prescriptions (e-prescription), with a focus on enhancing healthcare interoperability. The investigation encompasses two major domains: ontology overview and healthcare interoperability using semantic e-prescription. In the ontology overview, we scrutinize various aspects of ontology development, including the methodologies, languages, tools, and evaluation metrics adopted from literature. Notable comparisons between ontologies and databases are explored. Additionally, we delve into the challenges associated with ontology development and provide a comprehensive summary of methodologies, languages, tools, and evaluation approaches. Healthcare interoperability using semantic e-prescription undertakes a detailed review of e-prescription systems, emphasizing their critical role in healthcare interoperability. A thorough examination of frameworks facilitating semantic e-prescription is presented, offering a nuanced perspective on their contributions and limitations. The section concludes with a concise summary of the key findings from the e-prescription framework review. The article further addresses challenges in healthcare interoperability, including data standardization and system integration issues. To direct continuing research efforts that integrate cutting-edge technologies and interdisciplinary collaborations, future directions and emerging trends are outlined.]]>
<![CDATA[An innovative approach for predictive modeling and staging of chronic kidney disease ]]>
<![CDATA[Boughougal, Safa]]>;
<![CDATA[Laouar, Mohamed Ridda]]>;
<![CDATA[Siam, Abderrahim]]>;
<![CDATA[Eom, Sean]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp684-707
<![CDATA[Diagnosing silent diseases such as chronic kidney disease (CKD) at an early stage is challenging due to the absence of symptoms, making early detection crucial to slowing disease progression. This study addresses this challenge by introducing a novel feature, the estimated glomerular filtration rate (eGFR), calculated using the modification of diet in renal disease (MDRD) formula. We enriched our dataset by incorporating this feature, effectively increasing the volume of data at our disposal. eGFR serves as a critical indicator for diagnosing CKD and assessing its progression, thereby guiding clinical management. Our focus is on developing machine learning and deep learning models for the efficient and precise prediction of CKD. To ensure the reliability of our approach, we employed robust data collection and preprocessing techniques, resulting in refined information for model training. Our methodology integrates various machine learning and deep learning models, including four machine learning algorithms: adaptive boosting (AdaBoost), random forest (RF), Bagging, and artificial neural network (ANN), as well as a hybrid model. Our proposed ANN_AdaBoost model not only introduces a novel perspective by addressing an identified gap but significantly enhances CKD prediction.]]>
<![CDATA[Advanced predictive models for thyroid disease comorbidities using machine learning and deep learning: a comprehensive review ]]>
<![CDATA[Yacoob B. A., Mohammed]]>;
<![CDATA[J., Jayashree]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp673-683
<![CDATA[With advances in machine learning (ML) and deep learning (DL), the future of thyroid disease diagnosis and prognosis looks very bright. The integration of various data such as imaging and medical record data has increased the accuracy of the model. Advanced DL models such as convolutional neural network (CNN) and recurrent neural network (RNN) further improved disease detection in precision medicine. However, some of the major disadvantages of effective clinical integration include unbalanced samples, unclear sampling, having to communicate in different populations, decreased physician confidence due to the vagueness of current models therefore, and few studies available to identify thyroid comorbidities such as polycystic ovary syndrome (PCOS) and thyroid eye disease (TED) in a variety of different populations to develop the line. It is important to focus future research activities on model definition and validation an improving and thus the diagnosis and prognosis of thyroid comorbidities is of utmost importance. What this will bring is ML and DL, an opportunity to make very significant improvements in the diagnosis, treatment, and management of thyroid diseases, thereby improving patient outcomes and health care by seeking crystals as a group they work interdisciplinary to collaborate in developing flexible solutions, sharing knowledge, and responding to these stated deficiencies.]]>
<![CDATA[Malware detection using Gini, Simpson diversity, and Shannon-Wiener indexes ]]>
<![CDATA[Ling, Yeong Tyng]]>;
<![CDATA[Chiew, Kang Leng]]>;
<![CDATA[Phang, Piau]]>;
<![CDATA[Zhang, Xiaowei]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp737-750
<![CDATA[The increasing number of malware attacks poses a significant challenge to cyber security. This paper proposes a methodology for static malware analysis using biodiveristy-inspired metrics that is Gini coefficient, Simpson diversity, and Shannon-Wiener index for malware detection. These metrics are used to build the structural feature representation on the raw binary file as the feature space. The effectiveness of these metrics are evaluated using multilayer perceptron (MLP) neural network and extreme gradient boosting (XGBoost) models. A deterministic algorithm is used to generate these features that represent the feature signature of the executable file. Additionally, we investigated the effectiveness of different byte sizes as the input feature for these two classifiers. According to the results, Gini coefficient with on chunk size of 128 has successfully achieved average F1 score of more than 98.7% by using XGBoost model.]]>
<![CDATA[Pioneering the digital readiness for Malaysian museums: custom framework ]]>
<![CDATA[Khan, Rehman Ullah]]>;
<![CDATA[Sze Yee, Sabine Chung]]>;
<![CDATA[Bee, Oon Yin]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i2.pp728-736
<![CDATA[A museum is a hub for public exploration and education of community or country culture and traditions. Digital technologies transform museums into interactive experiences, engaging visitors and bringing cultural values to life. However, Malaysian museums struggle to adopt digital technologies due to limited infrastructure, expertise, exhibition technology, and budgets. These constraints hinder effective audience engagement and limit growth and modernisation efforts. To help Malaysian museums in digitalisation, this study aims to contextualise a digital readiness index (DRI) questionnaire. The findings of this pioneering study have yielded a unique and customised version of the DRI questionnaire specifically designed for Malaysian museums, marking the first-ever initiative of its kind in the country. The DRI serves as a pivotal scale or tool for managers and researchers, facilitating the evaluation and validation of a museum’s digitalisation status while guiding strategic planning for future advancements. This questionnaire enables researchers and museum managers to gain insights into the museums and understand which dimensions require focus and enhancement to ensure a successful and comprehensive transition towards digital transformation.]]>
