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[Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications ]]>
<![CDATA[Shiva Kumar, Badiganchela]]>;
<![CDATA[Reddy, Galiveeti Umamaheswara]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp1189-1198
<![CDATA[Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.]]>
<![CDATA[Performance analysis of D2D network in heterogeneous multitier interference scenarios ]]>
<![CDATA[Santhakumar, Dhilipkumar]]>;
<![CDATA[Chellaperumal, Arunachalaperumal]]>;
<![CDATA[Lazer Jessie, Jenifer Suriya]]>;
<![CDATA[Arulpragasam, Jerlin]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp811-821
<![CDATA[The trade-off between boosting network throughput and minimizing interference is a critical issue in fifth generation (5G) networks. Diverting the data traffic around the network access point in device-to-device (D2D) communication is an important step in realizing the vision of 5G. Though the D2D network improves the network performance, they complicate the interference management process. Interference is an invisible physical phenomenon occurring in wireless communication which happens when multiple transmissions happen simultaneously over a general wireless medium. Enormous growth in usage of mobile phone and other wireless gadgets in recent years has paved the way for significant role in Interference analysis over multi-tier network. Interference could affect communication systems performance and it might even prevent systems functioning properly. 3G and 4G wireless devices coexisted with reverse compatibility in a coverage area. Also, after their widespread adoption, 5G devices have become prevalent across the globe and this reaffirms interference coexistence as a significant field of research. Multiple systems operating in a region will cause severe interference and ultimately reduce the quality of received signal. A simulation environment for cellular standards coexistence considering real-time parameters is created and experimented. Various research works earlier addresses the interference mitigation techniques in multi-tier networks but none of them present the analysis of scenarios and interfering signal power levels in the respective contexts. In this paper various scenarios with different network interference coexistence were studied, simulated, and analyzed quantitatively.]]>
<![CDATA[Classification of breast cancer using a precise deep learning model architecture ]]>
<![CDATA[Ghazal, Mohammed]]>;
<![CDATA[Al-Ghadhanfari, Murtadha]]>;
<![CDATA[Fadhil, Fajer]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp933-940
<![CDATA[Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.]]>
<![CDATA[Review of NLP in EMR: abbreviation, diagnosis, and ICD classification ]]>
<![CDATA[Iqbal Basheer, Nurul Anis Balqis]]>;
<![CDATA[Nordin, Sharifalillah]]>;
<![CDATA[Kasim, Sazzli Shahlan]]>;
<![CDATA[Ali, Azliza Mohd]]>;
<![CDATA[Abdul Hamid, Nurzeatul Hamimah]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp881-891
<![CDATA[This review explores state-of-the-art natural language processing (NLP) methods applied to electronic medical records (EMRs) for key tasks such as expanding medical abbreviations, automated diagnosis generation, international classification of diseases (ICD) classification, and explaining model outcomes. With the growing digitization of healthcare data, the complexity of EMR analysis presents a significant challenge for accurate and interpretable results. This paper evaluates various methodologies, highlighting their strengths, limitations, and potential for improving clinical decision-making. Special attention is given to abbreviation expansion as a crucial step for disambiguating terms in the clinical text, followed by an exploration of auto-diagnosis models and ICD code assignment techniques. Finally, interpretability methods like integrated gradients and attention-based approaches are reviewed to understand model predictions and their applicability in healthcare. This review aims to provide a comprehensive guide for researchers and practitioners interested in leveraging NLP for clinical text analysis.]]>
<![CDATA[Spth-FCM: decision support tool for speech therapist based on fuzzy cognitive mapping ]]>
<![CDATA[Asma, Maziz]]>;
<![CDATA[Cherif, Taouche]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp845-859
<![CDATA[The development and integration of medical information systems into a unified information space is a significant focus in the field of information technologies. It is essential to develop decision support systems (DSS) to enhance the effectiveness of medical and diagnostic procedures. This article presents a novel decision support tool for speech therapists, which is based on fuzzy cognitive maps (FCM). The latter is a method of modeling complex systems using knowledge of human existence and experience. The proposed tool is composed of three phases. The first phase focuses on entering patient information into the graphical interface developed in JAVA based on the most precise observations. An FCM will be automatically constructed, describing the type of disorder and the patient’s case during the second phase. Finally, in the third phase, FCM-based scenarios were built during the execution of the inference process under FCM expert. The system is presented and demonstrated using a real cases study for eight weeks. The results show that the tool makes it possible to display, guide, assist, and confirm the medical decision of the speech therapist for an appropriate diagnosis and treatment.]]>
<![CDATA[An artificial intelligent system for cotton leaf disease detection ]]>
<![CDATA[Jadhav, Priyanka Nilesh]]>;
<![CDATA[Patil, Pragati Prashant]]>;
<![CDATA[Sureja, Nitesh]]>;
<![CDATA[Chaudhari, Nandini]]>;
<![CDATA[Sureja, Heli]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp950-959
<![CDATA[This study aims to develop a deep learning-based system for the detection and classification of diseases in cotton leaves, with the goal of aiding in early diagnosis and disease management, thereby enhancing agricultural productivity in India. The study utilizes a dataset of cotton leaf images, classified into four categories: Fusarium wilt, Curl virus, Bacterial blight, and Healthy leaves. The dataset is used to train and evaluate various CNN models such as basic CNN, VGG19, Xception, InceptionV3, and ResNet50. These models were evaluated on their accuracy in identifying the presence of diseases and classifying cotton leaf images into the respective categories. The models were trained using standard deep learning frameworks and optimized for high performance. The results indicated that ResNet50 achieved the highest accuracy of 100%, followed by InceptionV3 with 98.75%, and VGG19 and Xception both with 97.50%. The basic CNN model showed an accuracy of 96.25%. These models demonstrated strong potential for accurate multi-class classification of cotton leaf diseases. This study emphasizes the potential of deep learning in agricultural diagnostics. Future research can focus on improving model robustness, incorporating larger datasets, and deploying the system for real-time field use to assist farmers in disease management and improving cotton production.]]>
<![CDATA[Scaling of Facebook architecture and technology stack with heavy workload: past, present and future ]]>
<![CDATA[Sutikno, Tole]]>;
<![CDATA[Ahmad, Laksana Talenta]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp772-782
<![CDATA[Leading social media Facebook has improved its architecture to meet user needs. Facebook has improved its systems to handle millions of users with heavy workloads and large datasets using innovative architectural solutions and adaptive strategies. The study examines Facebook’s architectural and technological advances in heavy workload and big data. To understand how Facebook scaled with a growing user base and data volume, history and system architecture will be examined. It will also examine how cloud storage and high-performance computing optimize resource utilization and maintain performance during peak user activity. Facebook is managing big data and heavy workloads with new technologies like the hybrid communication model that uses PULL and PUSH strategies for real-time messaging. Facebook switched from HBase to MyRocks for message storage to improve performance as data grew. Architectural scaling and technology stack research must prioritize data storage innovations and optimized communication protocols to handle heavy workloads and big data. The messenger Sync protocol reduces network congestion and improves synchronous communication, reducing resource consumption and maintaining performance under high load. High-performance computing (HPC) and cloud storage should be studied together to support complex compute workflows. This convergence may improve large-scale application infrastructures and encourage interdisciplinary collaboration for scalable and resilient systems.]]>
<![CDATA[Chatbot for virtual medical assistance ]]>
<![CDATA[Chaithanya, Aravalli Sainath]]>;
<![CDATA[Vishista, Sampangi Lahari]]>;
<![CDATA[MadhuSri, Adepu]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp914-922
<![CDATA[A healthy population is vital for societal prosperity and happiness. Amidst busy lifestyles and the challenges posed by the COVID-19 pandemic, individuals often neglect their health needs. To address this, we introduce a novel approach utilizing a chatbot for virtual medical assistance. Tailored for individuals confined indoors or hesitant to visit hospitals for minor ailments, our chatbot offers personalized medical support by diagnosing ailments based on user-reported symptoms and engaging in interactive conversations. Leveraging a robust dataset containing 132 symptoms, 41 diseases, and corresponding medications, our chatbot employs a systematic approach for symptom refinement, enhancing diagnostic precision. Upon identifying a disease, the chatbot promptly suggests basic medications tailored to the specific ailment. Furthermore, our system integrates user demographics to evaluate medication history and current state, allowing for personalized medication recommendations based on individual needs. Through extensive testing and validation, we demonstrate the effectiveness of our chatbot in accurately predicting ailments and providing timely treatment advice. Our study introduces a novel paradigm for medicine recommendation and disease prediction, with the potential to enhance healthcare accessibility and effectiveness.]]>
<![CDATA[Soil moisture prototype soil moisture sensor YL-69 for Gaharu (Aquilaria malaccensis) tree planting media ]]>
<![CDATA[Kartadie, Rikie]]>;
<![CDATA[Nugroho, Muhammad Agung]]>;
<![CDATA[Prayitna, Adiyuda]]>;
<![CDATA[Kusjani, Adi]]>;
<![CDATA[Mardika, Ardeana Galih]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp1163-1171
<![CDATA[Soil moisture, defined as the amount of water present in the spaces between soil particles, plays a critical role in plant growth. Excessive soil moisture can lead to issues such as root rot, deviating from the ideal conditions required for root absorption. To address this, we developed a prototype tool using the YL-69 soil moisture sensor to monitor and control the soil moisture levels in Agarwood/Gaharu tree planting media. The prototype was designed to activate a water pump when soil moisture exceeded 80%, ensuring optimal humidity for plant growth. Once the moisture level dropped below 80%, the pump was deactivated to prevent overwatering. The YL-69 sensor demonstrated an accuracy of 88.76% under controlled conditions. This study highlights the potential of using low-cost sensors for automated soil moisture management in small-scale Gaharu cultivation.]]>
<![CDATA[The bootstrap procedure for selecting the number of principal components in PCA ]]>
<![CDATA[Toleva, Borislava]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v14i3.pp1136-1145
<![CDATA[The initial step in determining the number of principal components for both classification and regression involves evaluating how much each component contributes to the total variance in the data. Based on this analysis, a subset of components that explains the highest percentage of variance is typically selected. However, multiple valid combinations may exist, and the final choice is often made manually by the researcher. This study introduces a novel yet straightforward algorithm for the automatic selection of the number of principal components. By integrating ANOVA and bootstrapping with principal component analysis (PCA), the proposed method enables automatic component selection in classification tasks. The algorithm is evaluated using three publicly available datasets and applied with both decision tree and support vector machine (SVM) classifiers. Results indicate that this automated procedure not only eliminates researcher bias in selecting components but also improves classification accuracy. Unlike traditional methods, it selects a single optimal combination of principal components without manual intervention, offering a new and efficient approach to PCAbased model development.]]>
