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 - Venkata Sai Varsha, Prodduturi Bhargavi, Samah Senbel Abstract - This paper provides a thorough analysis of U.S. Congressional history from the 66th to the 118th Congress, examining demographic trends, political shifts, and party dynamics across decades. Using Python-based data processing, the study compiles and interprets historical data to identify patterns in representative demographics, party representation, and legislative impact. The analysis investigates generational changes within Congress, with particular focus on age distribution, tenure, and shifts in political party dominance. Visualizations and statistical insights generated through Python libraries, such as Pandas, Matplotlib, and Seaborn, reveal significant historical events and socio-political influences shaping Congress. By examining age-related trends, the study highlights a generational gap, with older members retaining significant representation and a younger cohort gradually emerging. Additionally, it explores the evolution of bipartisan dominance and third-party representation, offering insights into political diversity and the resilience of the two-party system. This research contributes to the understanding of how demographic and political transformations within Congress reflect broader societal trends and may influence future governance.
Authors - Taufique Hedayet, Anup Sen, Mahfuza Akter Jarin, Shohel Rana Shaon, Joybordhan Sarkar, Sadah Anjum Shanto Abstract - A price hike is an atypical increase in the cost of an essential item. A price rise is an unusual increase in the prices of everyday basic goods. The price increase has several factors. Everyday items are becoming more and more expensive. In this research, we have used Bidirectional Long Short-Term Memory (BLSTM), Long Short-Term Memory (LSTM), Adaboost, Support Vector Regression (SVR), Gradient Boosted Regression Tree (GBRT), and REST API for forecasting the prices for necessary commodities and we will evaluate efficiency by the value of gold. Our preeminent objective is to find a method that can detect and predict price hike that can be much more accurate and efficient than the other approaches that are currently available in the relevant literature. The acceptance of the detection and prediction is based on their accuracy and efficiency. Price hike predictions may role important for everyday life for many stakeholders, including firms, consumers, and government. The energetic and sporadic character of advertising estimating is highlighted as a major foreseeing.
Authors - Sathvik Putta, Tejagni Chichili, Samah Senbel Abstract - Traffic accidents remain a critical issue globally, with significant implications for public health, safety, and economic stability. This study provides a comprehensive analysis of traffic accident trends in the northeastern United States, focusing on Connecticut and its neighboring states—New York, New Jersey, New Hampshire, and Massachusetts. By leveraging a dataset encompassing fatal collisions, driver behaviors, and car insurance premiums, this work investigates correlations between risky driving habits, accident outcomes, and the associated financial impacts. Key metrics analyzed include speeding-related incidents, alcohol-impaired driving, distracted driving, and their influence on insurance costs and claims. rigorous data preprocessing methodology was employed, including normalization, outlier detection, and feature selection, ensuring a robust and reliable dataset for analysis. The study used advanced visualization techniques and statistical modeling, utilizing Python libraries like Pandas, Matplotlib, and Scikit-learn, to identify trends and derive actionable insights. Comparative analysis reveals that while neighboring states such as Massachusetts and New York excel in certain safety metrics, Connecticut lags in addressing critical behavioral risks like speeding and alcohol impairment.
Authors - Dawngliani M S, Thangkhanhau H, Lalhruaitluanga Abstract - Breast cancer continues to pose a major public health challenge world-wide, necessitating the development of accurate prediction algorithms to improve patient outcomes. This study aimed to devise a predictive model for breast cancer recurrence using machine learning techniques, with data sourced from the Mizoram State Cancer Institute. Utilizing the Weka machine learning toolkit, a hybrid approach incorporating classifiers such as K-Nearest Neighbors (KNN) and Random Forest was explored. Additionally, individual classifiers including J48, Naïve Bayes, Multilayer Perceptron, and SMO were employed to evaluate their predictive efficacy. Voting ensembles are utilized to augment performance accuracy. The hybridization of Random Forest and KNN classifiers, along with other base classifiers, demonstrated notable improvements in predictive performance across most classifiers. In particular, the combination of Random Forest with J48 yielded the highest performance accuracy at 82.807%. However, the J48 classifier alone achieved a superior accuracy rate of 84.2105%, signifying its efficacy in this context. Thus, drawing upon the analysis of the breast cancer dataset from the Mizoram State Cancer Institute, Aizawl, it was concluded that J48 exhibits the highest predictive accuracy compared to alternative classifiers.
Authors - Cansu Cigdem EKIN, Mehmet Afsin YUCE, Emrah EKMEN, Gokay GOK, Ibrahim UGUR Abstract - This study presents a preliminary assessment of the reliability and validity of a technology acceptance model UTAUT (Unified Theory of Acceptance and Use of Technology) for KeyDESK, a health facility management system used in healthcare settings. The model evaluates key constructs of the UTAUT model to better understand the contextual adoption of health facility management systems. Data were collected from 2547 respondents comprising system operators and healthcare professionals who utilize the KeyDESK platform for task and service management. Reliability was assessed through internal consistency measures, which confirmed strong alignment across constructs. Convergent validity was established by evaluating shared variance and item relevance, while the distinctiveness of constructs was verified through cross-comparative analyses. Preliminary results suggest that all constructs fulfill reliability and validity criteria, ensuring the robustness of the measurement model. These results provide an empirical foundation for understanding user acceptance of health facility management systems and highlight areas for further model refinement. This study serves as a critical step towards conducting more comprehensive structural equation modeling (SEM) analyses in subsequent research.
Authors - Kwang Sik Chung, Jihun Kang Abstract - As distance education services develop, much research is being conducted to analyze learners' learning activities and provide a customized learning environment optimized for each individual learner. The personalized learning environment is basically determined based on learner-centered learning analytics. However, learning analysis research on learning content, which is the subject of interaction with learners, is insufficient. In order to recommend learning content to learners and provide the most appropriate learning evaluation method, learner's learning capability and the difficulty of the learning content must be appropriately analyzed. In this research, the relative learning difficulty of the learning contents and the learner is analyzed, and through this, the learner-relative learning contents difficulty is analyzed. For this purpose, educational (learning) contents Data, Learning Operational Data, Learner Personal Learning data, Peer Learner Group Data, and Learner Statistical Data are collected, stored at learning records storage server and analyzed by the Learning Analytics System with several Deep Learning models. Finally, we find the absolute difficulty of the subject, the relative difficulty of the subject, the relative difficulty of the peer learner group, the relative learning capability of a learner, the absolute learning capability of a learner, the learning contents relative difficulty level for each learner, and the absolute difficulty of the subject for each individual learner, and personalized learning contents are created and decide with them.
Authors - Omar Hamid, Homaiza Saud Ahmad, Ahmed Albayah, Fatima Dakalbab, Manar Abu Talib, Qassim Nasir Abstract - The science of photogrammetry has been developing rapidly in recent years. With the rise of tools adopting this science and the advancement of computer vision technologies, the potential of such software is being acknowledged by researchers and integrated by market professionals into various fields. To cope with the rapid changes and expanding range of photogrammetry tools, a methodology was developed to identify the most widely adopted software tools, whether open-source or commercial, by the research community and market professionals. This resulted in the identification of 37 tools for which we developed a comprehensive review and presented our findings through visualizations such as pie charts and graphs. Furthermore, a comparison between the tools was carried out based on seven different attributes describing them, in order to assist professionals and individuals in picking software for specific use cases.
Authors - Mona Kherees, Karen Renaud, Dania Aljeaid Abstract - Smart Tourism is the most rapidly expanding economic sector, with data serving as the foundation of all Smart Tourism operations when travelers participate in various tailored travel services before, during, and after their journeys. The massive volume of data collected through various Smart Tourism Technologies raises tourists’ concerns. They might adopt privacy-preserving behaviors, like restricting sharing, fabricating data, or refusing to disclose requested information. Consequently, service providers manipulate users into disclosing personal data by employing persuasive marketing techniques based on Cialdini’s principles. This research aimed to investigate how the persuasion strategies of Cialdini employed by tourism organizations or service providers influence privacy concerns and users’ willingness to share personal information. A mixed-methods approach, incorporating expert reviews, was utilized to propose and validate a framework based on the Antecedents-Privacy Concerns-Outcome (APCO) model.
Authors - Otshepeng Lethabo Malebye, Tevin Moodley Abstract - This paper explores integrating Knowledge Management principles within Project Management frameworks to address critical challenges project teams face, such as diminishing individual experience and employees applying their knowledge to the projects in which they are a part. This paper identifies common problems encountered in knowledge sharing, such as tacit knowledge externalisation and documentation within project environments, by exploring the KM principles and their relevance to project success. A proposed solution is presented by looking at existing systems, such as DocuWare and frameworks, Knowledge Management, and Project Management. This paper introduces a framework to demonstrate the significance of employing systematic processes for identifying, capturing, sharing, and applying knowledge within project teams. It utilises techniques such as interviews, post-project reviews, communities of practice, and training. By using the integrated approach, the proposed solution aims to solve knowledge silos, facilitate tacit knowledge externalisation, and improve knowledge documentation.
Authors - Ahamed Nishath S, Murugeswari R Abstract - Researchers in the field of artificial intelligence are increasingly interested in exploring how to spot and counteract the spread of fake news. When compared to machine learning approaches, deep learning methods are superior in terms of their ability to reliably identify instances of false news. This study analyses the efficacy of various neural network topologies in the classification of news items into two distinct categories: false and real. This work takes into a hybrid model that merges both CNN and RNN layers incorporate with multi-channel mechanism, Which is the most complex model. When determining model’s overall performance, criteria such as accuracy, precision, and recall rates are taken into consideration. According to the findings, the hybrid model is able to efficiently attain a high degree of accuracy, particularly 99.16% of the target accuracy. The aforementioned results highlight the adaptability of various neural network designs in the context of distinguishing between real and false news, hence revealing key insights that have the potential to be implemented in practical scenarios involving the verification of information and the evaluation of its validity.
Authors - Massimo Carlini, Giuseppina Anatriello, Elisabetta Cicchiello Abstract - The modern business context and the amount of data available to companies and organizations has made decision-making processes even more com-plex and articulated. This pushes companies to provide a better product or service for customers, reasoning in terms of quality, flexibility and responsiveness to their requests and needs. In this context, the concepts of customer centricity and satisfaction are placed, or the need for companies to try to satisfy demand by offering efficient and quality treatment aimed at satisfying customer needs based on a deep and solid knowledge of them. This paper reports on the activities carried out by Anas S.p.A., by Customer Ser-vice, over the last few years, to improve the Digital Customer Experience, making available to customers the knowledge and experience acquired over the years. The objective, in terms of Customer Centricity, was to put the customer at the center of the offer, providing them with more modern, innovative, intelligent and efficient dialogue tools.
Authors - Aniko Vagner Abstract - NoSQL databases are grouped into many categories, one of them is the key-value databases. Our goal is to examine whether a system-independent key-value logical model exists. The idea came from the Redis database, which has the opaque key-value type named string, but it supports lists, hashes, sets, sorted sets, etc. If we compare them to the document databases storing JSON documents, they can have a system-independent logical model. We gathered databases said to fall into the key-value category and read their documentation considering the stored data structures. We found many subcategories under the key-value category. We found that the clean key-value databases with buckets can have a system-independent database model where the buckets collect the key-value pair, and the model is so easy. We could not identify a system independent logical model for the rest subcategories. Additionally, we recognised some viewpoints on which the data model of the key-value databases can be examined. Altogether, considering all subcategories we cannot speak about a system-independent logical data model for key value databases.
Authors - Timi Heino, Sampsa Rauti, Sammani Rajapaksha, Panu Puhtila Abstract - Today, web analytics services are widely used on modern websites. While their main selling point is to improve the user experience and return of investment, de facto it is to increase the profits of third-party service providers through the access to the harvested data. In this paper, we present the current state-of-the-art research on the use of web analytics tools, and what kind of privacy threats these applications pose for the website users. Our study was conducted as a literature review, where we focused on papers that described third-party analytics in detail and which discussed their relation to user privacy and the privacy challenges they pose. We focused specifically on papers dealing with the practical third-party analytics tools, such as Google Analytics or CrazyEgg. We review the application areas, purposes of use, and data items collected by web analytics tools, as well as privacy risks mentioned in the literature. Our results show that web analytics tools are used in ways which severely compromise user privacy in many areas. Practices such as collecting a wide variety of unnecessary data items, storing data for extended periods of time without a good reason and not informing users appropriately are common. In this study, we also give some recommendations to alleviate the situation.
Authors - Abigail Gonzalez-Arriagada, Ruben Lopez-Leiva, Connie Cofre-Morales, Eduardo Puraivan Abstract - The rapid advancement of information and communication technologies (ICT) has created a significant digital divide between older adults and younger generations. This divide affects the autonomy of older adults in a digitalized world. To address this issue, various initiatives have attempted to promote their digital skills, which requires reliable tools to measure them. However, assessing these competencies in this age group presents complex challenges, such as developing scales that accurately reflect the dimensions involved. In this study, we present empirical evidence on the reliability and adaptation of the Assessment of Computer-Related Skills (ACRS) scale. We translated the instrument into Spanish and added descriptors to optimize its application. The evaluation included 54 older adults in Chile (39 women and 15 men, aged 55 to 80) in an environment designed for individualized observation during the performance of specific digital tasks. The analyses revealed that the five dimensions of the instrument have high reliability, with Cronbach’s alpha values between 0.959 and 0.968. Six items were identified whose removal could slightly improve this indicator. Overall, the scale shows excellent internal consistency, with a G6 coefficient of 0.9994. These results confirm that, both at the level of each dimension and as a whole, the instrument demonstrates strong internal consistency, reinforcing its utility for assessing the intended competencies. An additional contribution of this work is the public availability of the data obtained, with the aim of encouraging future research in this area. Given the nature of the scale, which allows for the assessment of skills across various computer-related tasks, evidence of its high internal reliability constitutes a valuable resource for designing more inclusive educational programs specifically tailored to the needs of older adults in digital environments.
Authors - Svetlin Stefanov, Malinka Ivanova Abstract - The advent of new technologies leads to a complexity of the cyber-crime landscape and scenes, which requires an adequate response from digital forensic investigators. To support their forensic activities, a number of models and methodologies have been developed, such as the methodology Digital Forensics Investigation from Practical Point of View DFIP, proposed by us in a previous work. In addition, there is an urgent need for a virtual environment that would organize and manage the activities of investigators related to communication, document exchange, preparation of computer expertise, teamwork, information delivery and training. In this context, a software system implementing the DFIP methodology has been developed, and the aim of the paper is to present the results of a study regarding the opinion and attitudes of forensic experts on the usefulness and role of the software system during the different phases of digital forensic investigation.
Authors - Timi Heino, Robin Carlsson, Panu Puhtila, Sammani Rajapaksha, Henna Lohi, Sampsa Rauti Abstract - Electronics is one of the most popular product categories among consumers online. In this paper, we conduct a study on the thirdparty data leaks occurring in the websites of the most online electronics stores used by Finnish residents, as well as the amounts of third parties present at these websites. We studied the leaks by recording and analyzing the network traffic happening from the website while conducting actions at the website that the normal user does when purchasing the product. We also analyze dark patterns found in these websites’ cookie consent banners. Our results show that in 80% of the cases, the product name, product ID and price were leaked to third parties along with the data identifying the user. Almost all of the inspected websites used dark patterns in their cookie consent banners, and privacy policies often had severe deficiencies in informing the user of the extent of data collection.
Authors - Luis E. Quito-Calle, Maria E. Barros-Ponton, Dalila M. Gonzalez-Gonzalez, Luis F. Guerrero-Vasquez, Jessica V. Quito-Calle Abstract - The confinement of families, whether due to health emergencies or other quarantines, has caused lifestyle changes to cause changes in the behavior of population and cause stress among its members when facing confinement. Present study aimed to determine if there is an association between the lifestyles and parents’ coping with stress due to confinement due to the Health Emergency or quarantine due to COVID- 19. This study methodology was quantitative, descriptive, correlational and cross-sectional. Participants were made up of 75 representatives of Bilingual Educational Institute "Home and School" INEBHYE. Instruments used were Lifestyle Profile Questionnaire (PEPS-I, in Spanish) and Stress Coping Questionnaire (CAE, in Spanish) with which it was obtained as a result that a healthy lifestyle predominates because families have been facing their stress under problem solving, positive reassessment and religion in the face of confinement. As a conclusion, it is obtained that there is a statistically significant association between the subscales of coping with stress and families lifestyle, which would imply a change in lifestyle to face the stress caused by confinement due to COVID-19.
Authors - Vicente A. Pitogo, Cristopher C. Abalorio, Rolyn C. Daguil, Ryan O. Cuarez, Sandra T. Solis, Rex G. Parro Abstract - The agricultural resources in the Philippines are essential for national food security and economic development with coffee being at its center. Moreover, recent data released by the Philippine Statistics Authority (PSA) show an increase in coffee production although there has been a worrying decline in pro-duction in Caraga region which grows over two thousand five hundred growers and has a huge area of land planted to coffee. The FarmVista project addressed this challenge through a data-driven approach by applying Principal Component Analysis (PCA) and various machine learning algorithms to classify and analyze coffee yield in Caraga. The study utilized a comprehensive dataset, the Coffee Farmers Enumerated Data, encompassing socio-demographic details, farming practices, and other influential factors. Gradient Boosting achieved the highest accuracy of 98.69%, with Random Forest closely following at 95.63%. These results highlight the effectiveness of advanced analytics and machine learning in improving coffee yield classification. By uncovering key patterns and factors affecting yield quality, this study provides valuable insights to optimize the coffee value chain in Caraga and addresses the region’s production challenges.
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.
