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.
