Journal of Emerging Technology and Digital Transformation

Synergistic Intelligence: A Stacking Ensemble Approach for Accurate and Scalable Diabetes Prediction

Irfanullah, Muhammad Shahan Ibad, Aamir Sohail — Wed, 31 Dec 2025 00:00:00 +0000

Diabetes mellitus poses a rapidly escalating global health crisis, currently affecting over 537 million adults and demanding scalable, automated diagnostic solutions. However, current machine learning interventions often face critical bottlenecks, particularly model overfitting and poor generalization due to severe class imbalance in clinical datasets. To overcome these limitations, this study engineers a robust, clinically applicable Stacking Ensemble framework validated on the Pima Indians Diabetes Dataset. We employed a rigorous data preprocessing pipeline that utilizes the Synthetic Minority Oversampling Technique (SMOTE) to rectify class distribution, ensuring unbiased decision boundaries. By strategically integrating the complementary strengths of Logistic Regression, Support Vector Machines, K-Nearest Neighbors, and Naive Bayes via a meta-learning architecture, our approach successfully mitigates the individual weaknesses of single classifiers. The proposed ensemble demonstrated superior performance, achieving an accuracy of 81.5% and a critical recall rate of 84.0%, significantly reducing the risk of missed diagnoses compared to baseline models. Crucially, the system maintains exceptional computational efficiency with an inference latency of only 27.43 ms, confirming its viability for real-time deployment in resource-constrained medical environments. This research bridges the gap between algorithmic complexity and practical utility, offering a scalable, interpretable solution for early diabetes detection.

A Scalable Intelligent Traffic Signal Framework Using IoT Sensing and Predictive ML Models

Aqsa Eman, Ahmad Khan, Saad Shahzad, Shahrukh Mushtaq — Wed, 31 Dec 2025 00:00:00 +0000

Traffic congestion in the urban setting is one of the biggest remaining challenges of modern cities. Rapid increase in population, increasing number of vehicles and insufficient development of road infrastructure as a whole has worsened congestion problems, leading to increase in travel time, high fuel consumption and high emission level and decline in overall urban living standards. Traditional fixed-time traffic signal controllers are based on predetermined schedules and are unable to adjust to real-time fluctuations in traffic flow and pedestrian behavior and unforeseen events such as accidents or road maintenance. These limitations make them less effective and contribute to inefficient traffic management. To overcome these problems, a Smart Traffic Light Control System with the integration of Internet of Things (IoT) sensor networks and advanced Machine Learning (ML) algorithms for dynamic, data-driven signal optimization is proposed in this study. The system continuously gathers and processes the real-time traffic data from the IoT devices such as Vehicle Count sensors, RFID units, surveillance cameras, environment sensors. These different data streams enable an accurate and comprehensive picture of the traffic behavior through multiple junctions to be created. ML models intend to take both the past and recent real-time traffic datasets and learn to forecast the level of congestion, approximate queue lengths and pinpoint the peak-hour travel flow patterns. Supervised learning methods (such as Random Forest, Gradient Boosting, LSTM network) are employed for traffic forecast, and the reinforcement learning algorithms are used for dynamic adjustment of signal time and phase time. This hybrid method of ML allows the system to self-adapt to ever-changing road conditions and distribute green light intervals in a more efficient way. The proposed model will strive to minimize traffic delays and increase the vehicle throughput, as well as road safety and the impact on the environment. It has a scalable architecture that enables it to interface with the current Intelligent Transportation Systems (ITS) and be extended to cover large urban networks. Experiments using simulation, backed up by actual traffic data, indicate significant betterment in average waiting time and signal responsiveness and movement of the whole traffic as compared to conventional fixed-time and actuated signal systems. These findings validate the hypothesis that the traffic signal management system based on the IoT and ML can introduce a ground-breaking solution to the optimization of urban mobility and congestion and facilitate the evolution and actualization of smarter and more sustainable cities.

AI-Driven Recruitment and Hiring Automation through CV Scanning and AI Agents

Nareta Kataria — Wed, 31 Dec 2025 00:00:00 +0000

The presented work is dedicated to the development
of an AI agent that will perform automated CV sorting and
evaluation to assist current hiring processes with a supervised
machine learning model. A Random Forest Regressor containing
300 trees was trained on structured features based on the
unstructured CV information including years of experience,
number of related skills, education level, overlap of skills with the
job description, and coverage ratio. The 50 actual CVs dataset
was divided to 80 percent training set and 20 percent testing
set and the ground truth target used in supervised learning
was HR inspired rule based scoring. The model realized an
impressive predictive power, as seen with the R² of 0.94 and Mean
Absolute Error of 4.46 which signifies a significant agreement
with human based ratings. The trained model is integrated to
an AI agent that automates the ranking of the candidates, short
listing and scheduling of interviews. The system is particularly
effective at unloading HR staff and increasing the efficiency of
the recruitment process; nevertheless, some of the key issues are
the bias in the training data, the tendency of the system to treat
different candidates with unequal consideration, and the limited
interpretability of the model. The study highlights the importance
of introducing feedback loops, reducing bias, and establishing
transparency to ensure that AI-based recruitment systems were
ethically, reliably, and humanly deployed.

Smart ATS: An AI-Driven Multi-Stage Resume Scoring and Recruitment Automation System

CHANDAN Kumar — Wed, 31 Dec 2025 00:00:00 +0000

An artificial intelligence-powered Applicant Track-
ing System (ATS) that uses a multi-step algorithmic pipeline
to handle candidate scoring, skill finding, experience analysis,
and resume extraction. The Sentence-BERT model (allMiniLM-
L6-v2) for job-description similarity, RapidFuzz for fuzzy skill
matching, canonical skill-mapping algorithms, and a determin-
istic experience-scoring model power the system’s hybrid scor-
ing architecture. Using weighted evaluation characteristics such
as skill relevance, experience alignment, LLM-based semantic
matching, and penalty adjustments for underqualification or
overqualification, the proposed ATS calculates a normalised 0–10
score. Experimental review on a dataset of over 40 resumes
demonstrates a screening accuracy improvement of over 88%
when compared to manual evaluation methodologies, significantly
reducing HR workload and producing consistent and intelligible
applicant rankings.