10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Libero Nigro, Franco Cicirelli Abstract - This paper proposes the Evolutionary Random Swap (ERS) clustering algorithm that extends the basic behavior of Random Swap (RS) by a population of candidate solutions (centroid configurations), preliminarily established through a proper seeding procedure, which provides the swap data points that RS uses in the attempting step of improving the current clustering solution. The new centroid solution improves the previous solution in the case it reduces the Sum of Squared Errors (SSE) index. ERS, though, can also be used to optimize (maximize), in not large datasets, the Silhouette (SI) coefficient which measures the degree of separation of clusters. High-quality clustering is mirrored by clusters with high internal cohesion and a high external separation. The paper describes the design of ERS that is currently implemented in parallel Java. Different clustering experiments concerning the application of ERS to both benchmark and real-world datasets are reported. Clustering results can be compared, for accuracy and execution time performance, to the use of the basic RS algorithm. Clustering quality is also checked with the application of other known algorithms.
Authors - Amir Ince, Saurav Keshari Aryal, Howard Prioleau Abstract - With the rise of social media, vast amounts of text, including code-switching, are being generated, presenting unique linguistic challenges for sentiment analysis. This study explores how existing models perform without fine-tuning to understand the challenges of analyzing code-switched data. We propose a prompt tuning approach based on generated versus human-labeled code-switched dataset. Our results show that the Few-shot technique and the Prompt Optimization Framework with Dataset Examples offer the most consistent performance, highlighting the importance of real-world examples and language-specific data in improving multilingual sentiment analysis. However, the studied models and technique do no exhibit the ability to significant triage sentiments for Hindi and Dravidian languages.
Authors - Hector Rafael Morano Okuno Abstract - The use of large language models (LLMs) has spread to various areas of knowledge. However, it is necessary to continue exploring them to determine their scope. In this work, an LLM is investigated to generate G-code programs for machine parts in Computer numerical control (CNC) milling machines. Prompt Engineering is employed to communicate with LLM, and a series of prompts are used to inquire about its scope. Among the results are the manufacturing operations that an LLM can program and the problems that arise in the developed G-codes. Finally, a sequence of steps is proposed to create G-codes using LLMs, and the prompt structures are shown to help users understand how the LLMs work when generating G-codes.
Authors - Hanaa Mohsin Ahmed, Muna Ghazi Abdulsahib Abstract - Fuzzy deep learning, which combines fuzzy logic and deep learning techniques to handle uncertainty and imprecision in the data as a first task and learn hierarchical representations of the data as a second task, is a promising method for feature data classification method with many usefully and important applications that meagres with several disciplines of knowledge. This work uses a fuzzy logic deep learning model to classify feature data on transmission casing data in specific. For the first time as an approach, fuzzy logic deep learning has been used to use transmission casing data, a well-known benchmark dataset application for classification tasks in specific. The results of the experiments show that the proposed model outperforms the deep learning-based classification model, classifying the transmission casing data with a higher accuracy of 100% and more robustness. We also go over potential future research directions for Transmission-based fuzzy deep learning feature data classification.
Authors - Titi Andriani, Chairul Hudaya, Iwa Garniwa Abstract - The transition toward more sustainable renewable energy sources has driven advancements in energy storage technology, including the development of Battery Energy Storage Systems (BESS). To improve the reliability and efficiency of BESS, implementing an effective monitoring system is essential, especially for detecting and diagnosing battery faults. The most commonly utilized methodologies for the diagnosis of faults in battery systems encompass knowledge-based, model-based, and data-based approaches. Artificial Intelligence (AI) holds significant potential to enhance fault diagnosis systems through predictive models capable of analyzing large datasets, identifying patterns, and forecasting potential faults. This work offers a thorough investigation of AI applications for BESS fault diagnosis, supported by an in-depth review of reliable sources such as Science Direct, IEEE Xplore, and Scopus. A total of 723 papers from scientific publications over the last five years were initially considered in this research. Following a rigorous screening process, including duplicate removal and the application of exclusion and inclusion criteria, 28 studies were selected for quantitative analysis. This study not only examines the types of faults that can be diagnosed but also assesses the challenges associated with recent advancements in this technology. In this context, the research identifies several aspects that have been applied within the theory of AI-based fault diagnosis for BESS and offers recommendations for further research. The results of this study are intended to aid in the creation of fault diagnosis systems that are more dependable and effective, which in turn will support the transition to cleaner and more sustainable energy.
Authors - Vasyl Yurchyshyn, Yaroslav Yurchyshyn Abstract - A living organism can be seen as a tool designed to perform specific functions, while both living and non-living matter represent distinct manifestations of nature. This work proposes considering living and non-living matter as physical systems, integrating existing scientific and technological advancements in the fields of physics, biology, and computer science. It suggests that scientific and technological developments in physical systems can also be applied to biological systems. The work addresses issues related to coding within living organisms and physical systems, and explores potential models for their functioning. The use of the golden ratio in living organisms and the potential benefits of applying these codes to physical systems are examined. Additionally, the refinement of physical quantities using the approaches discussed is addressed. Key issues in the modelling of living matter are highlighted, and various approaches to addressing these challenges are explored. The binary encoding and encoding based on π, e, and the golden ratio are considered.
Authors - Helvira Maharani Tresnadi, Rannie Oges Pebina, Permata Chandra Lagitha, Nurul Sukma Lestari Abstract - This research aims to analyze the relationships between career calling, adaptability, and awareness of STARA technology to provide insights into career development during this critical transition phase. The methodology employed in this research is quantitative, with data collected through online questionnaire surveys. The data was analyzed using partial least squares structural equation modeling (PLS-SEM) and Smart PLS software. The participants are students in Jakarta, with 413 respondents completing the survey. The findings indicate that both career exploration and self-efficacy have a positive influence on career adaptability. Furthermore, career exploration and self-efficacy significantly and positively affect career calling, while career calling positively affects career adaptability. The results also indicate that STARA Awareness reduces the influence of career calling on career adaptability, although the findings remain significant. The mediating variable demonstrates a positive and significant effect on the relationship between career exploration, self-efficacy, and career adaptability. The novelty of this research is that it examines career calling in school children, which is still rarely studied compared to employees, to help students recognize their potential and interests early on. For future research, it is recommended to investigate variables within a broader scope at the national and international levels.
Authors - ThiTuyetNga Phu, HongGiang Nguyen Abstract - Inspecting the compressive strength of buildings' concrete is essential for ensuring the safety of households. This paper examined the study samplers collected using the nondestructive testing (NDT) method combined with Ultrasonic Pulse Velocity (UPV) and Rebound Hammer (RH) tests to check the beams of some apartments over 30 years old. Firstly, research samples were deployed to analyze the level of data variation using the exploratory data analysis (EDA) method to assess the reliability and correlation of data samples. Next, the study focused on the prediction of concrete compressive strength deploying five functions of activation (AF) (tanhLU, tanh, leakyLU, reLU, and sigmoid) by using two deep learning models as long short-term memory (LSTM) and gated recurrent unit (GRU). Lastly, the experimental results showed that the GRU model combined with two kinds of hybrid AFs gave a fairly accurate prediction level; in contrast, the remaining AF showed acceptable results.
Authors - Prathyush Kiran Holla, Manish M, Purvi Hande, Akshay Anand, Nirmala Devi M Abstract - Integrated Circuits (IC) allows attackers to insert malicious implants called Hardware Trojans (HT). These Trojans leak information or alter circuit functionality. This threat is particularly critical in IoT devices, where compromised hardware can lead to drastic consequences across networks potentially exposing entire systems to data loss. Over the past decade, numerous Hardware Trojan Detection (HTD) methods have been developed which is crucial for securing IoT ecosystems, where detecting hardware-level threats early can prevent cascade failures. Current HTD techniques still face challenges with detection accuracy, class imbalance handling and high false positive/negative rates. We propose a HTD method using XGBoost, enhanced with focal loss to better handle class imbalance. XGBoost is combined with both graph-based and structural features to achieve higher accuracy compared to using each feature type individually. This approach is particularly valuable for IoT applications, where interconnected systems require robust detection methods. The proposed model, evaluated on an extensive dataset comprising of 41 combinational and sequential benchmark circuits, achieves an impressive accuracy of 98.85%, demonstrating superior performance in HT detection across diverse circuit architectures. Such high accuracy is essential for IoT deployments where false positives can trigger unnecessary disruptions across connected systems, and false negatives can leave critical infrastructure vulnerable to attacks.
Authors - Andriy Tevjashev, Oleksii Haluza, Dmytro Kostaryev, Anton Paramonov, Natalia Sizova Abstract - The study focuses on estimating the accuracy of aircraft positioning using an infocommunication network of optical-electronic stations (OES). The problem addressed is the numerical estimation of the shape and boundaries of the region where the aircraft is located, with a given probability, at any fixed time during video surveillance in optical and infrared frequency ranges. The method departs from the traditional assumption of normal distribution for random errors in aircraft location estimates and employs Chebyshev's inequality to construct upper bounds for the uncertainty region. It is shown that the dispersion ellipsoid, often used to estimate the metrological characteristics of OES, is a rough approximation of the actual region where the aircraft is located with a given probability. The following results were obtained: – a method for constructing the actual uncertainty region of an aircraft’s location, based on the statistical properties of random errors in video surveillance from each OES and their relative spatial arrangement to the aircraft at each surveillance moment; – a software implementation of the numerical method for constructing and visualizing upper estimates of the shape and boundaries of the uncertainty region in aircraft positioning, using the OES network for trajectory measurements.
Authors - Qian Jiang, Kin Wai Michael Siu, Jiannong Cao Abstract - Immersive technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (MR), are widely used in exhibitions to engage audiences. This study examines immersive technologies in the context of museum learning with a focus on exhibitions. This study screened and analyzed 104 research papers in this scope closely related to the topic of immersive technologies and museums, which were selected based on search results for four keywords-human behavior, immersive technologies, exhibitions, and embedded experiences-to clarify the impact of immersive technologies on visitor behavior from existing exhibition themes. We conceptualized immersive technologies and categorized the literature according to theme and technology to clarify the relationship between immersive technology applications and exhibition topics. Existing research identifies a positive correlation between immersive technology and positive visitor experiences; however, there is less research on immersive technology and museum learning for special populations, and assessment tools for evaluating the effectiveness of technological application in this context have yet to be tested. The method of co-occurrence is used to analyze what factors need to be considered for the application of immersive technologies in the context of museum learning. Ultimately, a framework for immersive technological application is summarized.
Authors - Unaizah Mahomed, Machdel Matthee Abstract - The use of Professional Social Media Platforms (PSMPs) has become more popular in recent years. As COVID-19 spread globally the world was forced to fast-track digitalisation, remote and hybrid working models as well as the need for online hiring. This systematic literature review aims to give insight into understanding the role of artificial intelligence (AI) algorithms in professional social media platforms as well as gauge a deeper understanding for the need of these AI algorithms. This systematic literature review incorporates findings from previously published peer-reviewed literature to understand how AI-driven systems are used to improve hiring through professional social media platforms. The contents of this review address benefits related to hiring that include but is not limited to, the applications of AI algorithms in PSMPs, candidate screen and sourcing, job matching, and efficiency, as well as some concerns such as algorithmic bias, user privacy, regulations and ethical considerations. Significant effects on stakeholders have also been addressed within this review as well as the gaps within the research.
Authors - Bruno Zaninotto, Carlos Eduardo Barbosa, Alice Fonseca Monteiro, Lucas Nobrega, Luiz Felipe Martinez, Matheus Argolo, Geraldo Xexeo, Jano Moreira de Souza Abstract - The dynamic between buyers and sellers in the retail sector often leads to conflicts, necessitating a deeper understanding of customer complaints. The Internet is where customers can voice their opinions to influence purchasing decisions and shape company reputations. Brazil, recognized among the top 10 countries with the highest expectations for e-commerce growth worldwide in 2022, demonstrates a rapidly expanding market ready for exploration. This study addresses the problem by applying Latent Semantic Analysis (LSA) to analyze complaints about Americanas S.A., a large retail company on the Reclame Aqui platform, using the company as a case study for broader methodological application. Our findings reveal significant uniformity in complaints across Brazil, primarily concerning order processing, delivery, and product quality. These insights offer actionable intelligence for retailers to refine their Customer Relationship Management strategies and for the government to strengthen consumer protection policies, demonstrating the utility of LSA in improving customer satisfaction and trust in the retail landscape.
Authors - Islam Oshallah, Mohamed Basem, Ali Hamdi, Ammar Mohammed Abstract - Question answering systems face critical limitations in languages with limited resources and scarce data, making the development of robust models especially challenging. The Quranic QA system holds significant importance as it facilitates a deeper understanding of the Quran, a Holy text for over a billion people worldwide. However, these systems face unique challenges, including the linguistic disparity between questions written in Modern Standard Arabic and answers found in Quranic verses written in Classical Arabic, and the small size of existing datasets, which further restricts model performance. To address these challenges, we adopt a cross-language approach by (1) Dataset Augmentation: expanding and enriching the dataset through machine translation to convert Arabic questions into English, paraphrasing questions to create linguistic diversity, and retrieving answers from an English translation of the Quran to align with multilingual training requirements; and (2) Language Model Fine-Tuning: utilizing pre-trained models such as BERT-Medium, RoBERTa-Base, DeBERTa-v3-Base, ELECTRA-Large, Flan-T5, Bloom, and Falcon to address the specific requirements of Quranic QA. Experimental results demonstrate that this cross-language approach significantly improves model performance, with RoBERTa-Base achieving the highest MAP@10 (0.34) and MRR (0.52), while DeBERTa-v3-Base excels in Recall@10 (0.50) and Precision@10 (0.24). These findings underscore the effectiveness of cross-language strategies in overcoming linguistic barriers and advancing Quranic QA systems.
Authors - Aziza Irmatova, Mukhabbatkhon Mirzakarimova, Dilafruz Iskandarova, Guli-ra'no Abdumalikova Abstract - In the today, the development of digital education is playing an important role in radically changing the education system and making learning processes more innovative, interactive and convenient. In particular, digital platforms are the main tools that can change the educational process. Through these platforms, students have the opportunity to study lessons anywhere and at any time, without being limited to traditional classrooms. From this point of view, the development and implementation of digital educational platforms in educational institutions is one of the urgent issues, and the success of this process largely depends on the Internet coverage in the country, investments in digital infrastructure, and the impact of government policy. This article empirically analyzes the impact of Internet coverage, investments in digital infrastructure, and government policy on the implementation of digital educational platforms in Uzbekistan. The measurement of government policy was carried out by assessing the public's assessment of government policy.
Authors - Baraa Hikal, Ahmed Nasreldin, Ali Hamdi, Ammar Mohammed Abstract - Hallucination detection in text generation remains an ongoing struggle for natural language processing (NLP) systems, frequently resulting in unreliable outputs in applications such as machine translation and definition modeling. Existing methods struggle with data scarcity and the limitations of unlabeled datasets, as highlighted by the SHROOM shared task at SemEval-2024. In this work, we propose a novel framework to address these challenges, introducing DeepSeek Few-shot Optimization to enhance weak label generation through iterative prompt engineering. We achieved high-quality annotations that considerably enhanced the performance of downstream models by restructuring data to align with instruct generative models. We further fine-tuned the Mistral-7B-Instruct-v0.3 model on these optimized annotations, enabling it to accurately detect hallucinations in resource-limited settings. Combining this fine-tuned model with ensemble learning strategies, our approach achieved 85.5% accuracy on the test set, setting a new benchmark for the SHROOM task. This study demonstrates the effectiveness of data restructuring, few-shot optimization, and fine-tuning in building scalable and robust hallucination detection frameworks for resource-constrained NLP systems.
Authors - Youssef Maklad, Fares Wael, Wael Elsersy, Ali Hamdi Abstract - This paper presents a novel approach to evaluate the efficiency of a RAG-based agentic Large Language Model (LLM) architecture in network packet seed generation for network protocol fuzzing. Enhanced by chain-of-thought (COT) prompting techniques, the proposed approach focuses on the improvement of the seeds’ structural quality in order to guide protocol fuzzing frameworks through a wide exploration of the protocol state space. Our method leverages RAG and text embeddings in a two-stages. In the first stage, the agent dynamically refers to the Request For Comments (RFC) documents knowledge base for answering queries regarding the protocol Finite State Machine (FSM), then it iteratively reasons through the retrieved knowledge, for output refinement and proper seed placement. In the second stage, we evaluate the response structure quality of the agent’s output, based on metrics as BLEU, ROUGE, and Word Error Rate (WER) by comparing the generated packets against the ground truth packets. Our experiments demonstrate significant improvements of up to 18.19%, 14.81%, and 23.45% in BLEU, ROUGE, and WER, respectively, over baseline models. These results confirm the potential of such approach, improving LLM-based protocol fuzzing frameworks for the identification of hidden vulnerabilities.
Authors - Tushar Vasudev, Surbhi Ranga, Sahil Sankhyan, Praveen Kumar, K V Uday, Varun Dutt Abstract - To guarantee the safety and effectiveness of medical supplies like blood and vaccinations, careful environmental monitoring is necessary throughout transit. Even while real-time monitoring has advanced, current systems sometimes lack strong predictive ability to foresee unfavorable circumstances. The XGBoost Ensemble for Medical Supplies Transport (XEMST), a unique stacking ensemble model created to predict interior humidity levels during travel, is presented in this paper to fill this gap. By utilizing XGBoost's outstanding predictive fusion capabilities, the model incorporates predictions from fundamental machine learning methods, including Support Vector Machine, Random Forest, Decision Tree, and Linear Regression. XEMST outperformed individual models with a Root Mean Squared Error (RMSE) of 2.22% and an R2 score of 0.96 when tested across 17 different transit situations. By enabling prompt responses, these predictive insights protect medical supply quality from environmental hazards. This study demonstrates how sophisticated ensemble learning frameworks have the potential to transform intelligent healthcare logistics.
Authors - Erick Verdugo, Andy Abad, Remigio Hurtado Abstract - Software defect prediction is crucial for reducing costs and improving quality. According to a Cutter Consortium report, software defects cause an estimated annual loss of $1.56 trillion in global productivity. Additionally, Tricentis reported that over 30% of software development projects failed due to undetected defects. Undetected defects can increase maintenance costs, delay deliveries, and compromise security, particularly in critical applications such as financial or medical systems. A significant challenge is dealing with imbalanced data, where there are more defect-free modules than defective ones, making detection difficult. This study proposes a four-phase approach: loading and transforming data, using balancing techniques, applying machine learning models, and explaining predictions. Techniques such as SMOTE, ADASYN, and RandomUnderSampling were used to balance the data, applied to models like Random Forest, Gradient Boosting, and SVM. The JM1 dataset, containing software quality metrics and 80% defect-free modules, was used for analysis. Data preprocessing involved imputation, encoding, and normalization. Results show that Random Forest and Gradient Boosting, combined with balancing techniques, achieved the best performance in defect identification. In the future, advanced algorithms such as XGBoost and LightGBM will be explored, and parameter optimization will be conducted to further enhance results. This approach aims to improve defect detection in software and to be applied in other fields.
Authors - Salma Mosaad Mohamed Elfeky, Mennaallah Nafady Ahmed Yehia, Ali Hamdi Abstract - This paper introduces a novel drone-based plant disease detection system optimized for efficient and scalable deployment using MLOps. Utilizing the CADI AI dataset for cashew crop disease classification, it includes automated workflows for iterative training, testing, and deployment across YOLO architectures (YOLOv5, YOLOv8, YOLOv9, and YOLOv10). Advanced data augmentation and incremental dataset expansion, growing from 757 training images to the full dataset, ensure fair evaluations and model optimization. YOLOv5 achieved a peak mAP@50 of 59.4%, followed by YOLOv8 with 50.1%. Iterative finetuning revealed YOLOv9’s superior insect detection performance (mAP@50: 70.9 %) and YOLOv10’s excellence in abiotic stress detection (mAP@50: 77.3%). This study highlights MLOps’ role in real-time model deployment and benchmarking, showcasing robust object detection capabilities and emphasizing iterative optimization and auto-deployment strategies to address dataset imbalance and enhance precision agriculture.
Authors - Ananya Deshpande, Akshay Angadi, Amulya H S, Adhokshaja R B, Nirmala Devi M Abstract - The increasing complexity of ICs and the reliance on external suppliers increase the risk of hardware Trojans, posing significant security threats. Traditional detection methods often fail due to limitations in addressing all potential vulnerabilities. This paper proposes a node compaction technique combined with an XGBoost classifier using features like Vulnerability Factor, Transition Probability, and SCOAP metrics to classify circuit nodes as Trojan-infected or Trojan-free. The compaction reduces execution time and improves real-time monitoring. The checker logic further validates the detection of Trojans by comparing the expected and observed functionality. Validation in TrustHub benchmark circuits demonstrates significant improvements in detection accuracy.
Authors - Kevin Lajpop Abstract - The Fast Fourier Transform (FFT) is a fundamental algorithm used in a wide range of applications, from signal processing to cryptography. With the increasing use of embedded and mobile devices, the need to optimize FFT performance has become crucial. This study focuses on the implementation of FFT on an ARM Cortex-A72 processor, leveraging NEON instructions, which are part of the SIMD (Single Instruction, Multiple Data) set. NEON instructions enable parallel operations, resulting in a significant improvement in execution times. Through a comparative analysis between implementations with and without NEON, a 99.99% reduction in execution time was demonstrated when using NEON, highlighting its effectiveness in applications that require high-speed processing, such as post-quantum cryptography.
Authors - Diego Loja, David Alvarado, Remigio Hurtado Abstract - Lung cancer, one of the leading causes of death worldwide, accounts for more than 2.2 million cases and nearly 1.8 million deaths. This type of cancer is classified into non-small cell lung carcinoma (NSCLC), the most common and slow-progressing type, and small cell lung carcinoma (SCLC), which is less common but highly aggressive [1]. In response to the urgency for rapid and accurate diagnosis, this work presents an innovative method for classifying PET images using the EfficientV2S model, combined with advanced data augmentation and normalization techniques. Unlike traditional methods, this approach incorporates visual explanations based on integrated gradients, enabling the justification of model predictions. The proposed method consists of three phases: data preprocessing, experimentation, and prediction explanation. The LUNGPETCT- DX dataset is utilized, comprising 133 patients distributed across three main classes: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The models are evaluated using quality metrics such as accuracy (78%), precision (82%), recall (78%), and F1-score (76%), highlighting the superior performance of EfficientV2S compared to other approaches. Additionally, integrated gradients are employed to visually justify predictions, providing critical interpretability in the medical context. For future work, the integration of CT images is proposed to enhance predictions, along with validation on larger datasets and optimization through fine-tuning, aiming to improve the model’s generalization and robustness
Authors - Ebin V Francis, T. Nirmala, Jiby Jose E. Abstract - This study investigates how media affected the career aspirations of College students in Kerala and finds a tendency associated with social media adjusting over conventional work. Using content analysis of digital media platforms, the research investigates how media content, trends, and narratives influence students’ perceptions of social media as a viable and desirable career path. This study seeks to determine what has changed over the last two decades, including whether peer effects, economic opportunities, or social acceptance are behind this shift. This study offers insights into how the career interests and preferences of the young generation in Kerala influenced by the media landscape can potentially impact the labour market and employment patterns among the youth in the region and aids in understanding the implications of media-influenced occupational aspirations and media patterns among the students in Kerala.
