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 - Vishnu Kumar Abstract - Cold chain logistics is the process of maintaining a controlled temperature throughout the storage and transportation of temperature-sensitive products. Ensuring the integrity of the cold chain is critical for the safety and efficacy of pharmaceutical (pharma) products. In the modern supply chain land-scape, the pharma industry involves many stakeholders, including Small and Medium-sized Enterprises (SMEs), which handle logistics, storage and retail operations. Despite the availability of advanced temperature monitoring technologies, SMEs face significant challenges in adopting these solutions due to economic constraints, limited technological resources, and lack of expertise. To bridge this gap, this work proposes a novel, cost-effective Internet of Things (IoT) based framework for real-time temperature monitoring in the cold chain of pharma products. Using a Raspberry Pi and Sense HAT module, coupled with a smartphone application, this system enables SMEs to implement an affordable and reliable cold chain monitoring solution. The capabilities of the proposed framework are demonstrated through a temperature monitoring case study, simulating the conditions faced in pharma supply chains. This work is expected to provide a practical resource for SMEs and suppliers seeking to im-prove their cold chain management without incurring excessive costs.
Authors - Simona Filipova-Petrakieva, Petar Matov, Milena Lazarova, Ina Taralova, Jean Jacques Loiseau Abstract - Plant disease detection plays a key role in modern agriculture, with significant implications for yield management and crop quality. This paper is a continuation of previous research by the authors' team related to the detection of pathologies on apple tree leaves. In order to eliminate the problem of overfitting in the traditional convolutional neural networks (CNNs) transfer learning layers are added to a residual neural network architecture ResNet50. The suggested model is based on pre-trained CNN whose weight coefficients are adapted until ResNet obtains the final classification. The model implementation uses Tensor- Flow and Keras frameworks and is developed in Jupyter Notebook environment. In addition, ImageDataGenerator is utilized for data augmentation and preprocessing to increase the classification accuracy of the proposed model. The model is trained using a dataset of 1821 high-resolution apple leaves images divided into four distinct classes: healthy, multiple diseases, rust, and scab. The experimental results demonstrate the effectiveness of the suggested ResNet architecture that outperforms other state-of-the art deep learning architectures in eliminating the overfitting problem. Identifying different apple leaves pathologies with the proposed model contributes to developing smart agricultural practices.
Authors - Malinka Ivanova, Svetlin Stefanov Abstract - The growing number and increasing complexity of cyberattacks require investigative experts to use contemporary technologies for finding and analyzing digital evidence and for preparing computer expertise. Artificial intelligence (AI) and machine learning (ML) are among the possibilities for automating a number of routine activities in digital forensics, which can be performed significantly faster and more efficiently. The aim of the paper is to present the potential of AI and ML at analyzing digital evidence as in this case the extraction of text and image information from pdf files is specifically examined. A classification of different types of files that could potentially be located on the victim’s or attacker’s smartphone or computer is also performed using ML algorithm Decision Tree. Synthetically generated files and original scientific papers are utilized for the experiments. The findings point out that the obtained accuracy at classification of file formats, at analyzing and summarizing the content of pdf files is high, which is done thought applying Natural Language Processing techniques and Large Language Models.
Authors - Enaam Youssef, Mahra Al Malek, Nagwa Babiker Yousif, Soumaya Abdellatif Abstract - Social media algorithms are important in suggesting content aligned with users' needs. The relevant technology suggests content and ensures its suitability and relevance to users. Consequently, it is considered an important aspect of everyday life in enhancing community and cultural identity among youth. This research examines the effect of social media algorithms on the community and cultural identity of the young generation in the United Arab Emirates. Theoretically supported by Social Identity Theory, this research gathered data from 341 respondents using structured questionnaires. Results indicated that Social Media Algorithms positively affect Community Identity, implying that these platforms promote a sense of belonging by connecting them to local groups, discussions, and events, strengthening their cultural and social community ties. Results also revealed that the effects of social media algorithms on cultural identity remain positively significant. These findings indicate that social media content improves connection to cultural heritage and shapes cultural identity perceptions, although algorithms sometimes prioritize global over local practices. Overall, these results indicate a robust influence of social media in the UAE as a factor enabling the young generation to seek community identity and cultural belonging, which further helps them retain their overall social identity in the best possible manner. Study findings and limitations are discussed accordingly.
Authors - Hayat Bihri, Soukaina Sraidi, Haggouni Jamal, Salma Azzouzi, My El Hassan Charaf Abstract - Predictive analytics and artificial intelligence (AI) offer significant potential to improve healthcare, yet challenges in achieving interoperability across diverse settings, such as long-term care and public health, remain. Enhancing Electronic Health Records (EHRs) with multimodal data provides a more comprehensive view of patient health, leading to better decision-making and patient outcomes. This study proposes a novel framework for real-time cardiovascular disease (CVD) risk prediction and monitoring by integrating medical imaging, clinical variables, and patient narratives from social media. Unlike traditional models that rely solely on structured clinical data, this approach incorporates unstructured insights, improving prediction accuracy and enabling continuous monitoring. The methodology includes modality specific preprocessing: sentiment analysis and Named Entity Recognition (NER) for patient narratives, Convolutional Neural Networks (CNNs)for imaging, and Min-Max scaling with k-Nearest Neighbors (k-NN) imputation for clinical variables. A unique patient identifier ensures precise data fusion through multimodal transformers, with attention mechanisms prioritizing key features. Real-time monitoring leverages streaming natural language processing (NLP) to detect health trends from social media, triggering alerts for healthcare providers. The model undergoes rigorous validation using metrics like AUC-ROC, AUC-PR, Brier score, SHAP values, expert re-views, and clinical indicators, ensuring robustness and relevance.
Authors - Quoc Hung NGUYEN, Xuan Dao NGUYEN THI, Thanh Trung LE, Lam NGUYEN THI Abstract - With the rapid development of financial technology, financial product recommendation systems play an increasingly important role in enhancing user experience and reducing information search costs, becoming a key factor in the financial services industry. Amid growing competitive pressure, the diversification of user needs, and the continuous expansion of financial products, traditional recommendation systems reveal limitations, especially in terms of accuracy and personalization. Therefore, this study focuses on applying deep learning technology to develop a smarter and more efficient financial product recommendation system. We evaluate this model based on key metrics such as precision, recall, and F1-score to ensure a comprehensive assessment of the proposed approach's effectiveness. Methodologically, we employ the Long Short-Term Memory (LSTM) model, a type of Recurrent Neural Network (RNN) designed to address the challenge of long-term memory retention in time-series data. For the task of recommending the next loan product for customers, LSTM demonstrates its ability to remember crucial information from the distant past, thanks to its gate structure, including input, forget, and output gates. Additionally, the model leverages a robust self-attention mechanism to analyze complex relationships between user behavior and financial product information.
