ABSTRACT:
The growth of online payment industries and Online businesses have brought forth some significant security challenges, especially when it comes to credit card use that is unauthorized. In this paper, an innovative framework that detects fraud in real-time has been presented, immediately alerting the stakeholders of suspicious to an activity. We compare several classification methods with real trans- action data, with the performance of the methods measured in terms of several important performance measures such as accuracy, precision, recall and F1-score. We find that the methods of ensemble learning provide this benefit over both standard algorithms, namely better accuracy and reduced levels of false positives. The combined warning system gives the user the opportunity to respond instantly to protect themselves. Our study shows that smart analytical systems can be used to enhance security procedures in modern financial ecosystems.
Keywords: Credit card fraud, machine learning, ensemble methods, real-time detection, alert system, imbalanced dataset.

ABSTRACT:
This paper presents a nano-power cascoded β-multiplier CMOS Voltage Reference without the use of passive filters. The circuit includes a start-up, output stage, β-multiplier circuit, bias current generator, and operational amplifier to achieve a high-power supply rejection ratio and low line sensitivity. The cascoded β-multiplier CMOS Voltage Reference is implemented on 180 nm of technology. The reference voltage is 430.23 mV operating from a wide supply range of 0.9 V to 3.0 V. The Power Supply Rejection Ratio is -90.86 dB at 1 kHz, and the line Sensitivity is 0.0008 %/V. Furthermore, the design achieves an ultra-diminished power consumption of only 379.16 nW, with a total output noise of 27.17 nV/√Hz and draws a mere 421.29nA of supply current at 0.9V. The measured TC was found to be 58.2 ppm/°C.
Keywords: Beta-Multiplier, Power Supply Rejection Ratio, Line Sensitivity, ultra-low power consumption, Temperature Coefficient

ABSTRACT:
In criminal investigation, today, digital images are significant; however, they are manipulable by using sophisticated editing software, and it is challenging to detect the manipulation. Traditional forensic methods mainly rely on hash algorithms to check for tampering. How-ever, these tools are unable to detect advanced tampering techniques such as copy-move attacks and metadata manipulation. This research proposes a hybrid model that combines different techniques to detect tampering, including cryptographic verification, error level analysis, metadata analysis, and deep learning classification. The system combines the results from all these methods using a weighted confidence scoring system, which calculates a final tampering probability score. Moreover, it includes an immutable Chain-of-Custody logging system to provide forensic traceability, addressing important gaps in modern judicial documentation requirements.
Keywords—Digital Forensics, Image Tampering, Law Enforcement, Deep Learning, Chain-of-Custody, Cryptography.

ABSTRACT:
ThisEarly detection of cardiovascular diseases is essential to reduce mortality and enable timely medical intervention. This paper proposes a real-time IoT-based heart disease prediction system that integrates multiple physiological sensors and machine learning algorithms for continuous health monitoring. The system incorporates a heartbeat sensor, Galvanic Skin Response (GSR) sensor, pulse oximeter for SpO₂ and pulse rate measurement, and a DS18B20 temperature sensor. Sensor data is acquired through the MCP3008 ADC and processed using a Raspberry Pi 4. A Random Forest machine learning model is employed to analyse physiological patterns and predict potential cardiac abnormalities. The system provides real-time visualization via an LCD display and sends alerts through a GSM module when abnormal conditions are detected. Experimental evaluation shows that the proposed model achieves an accuracy of 92.3%, precision of 91.1%, recall of 90.5%, and F1-score of 90.8%, demonstrating its effectiveness for early heart disease prediction and continuous monitoring.
Keywords: Heart Disease Detection, Machine Learning, IoT Healthcare, Real-Time Monitoring, Random Forest, Early Warning System.

ABSTRACT:
Deep learning models have achieved remarkable success in medical image analysis in recent times. However, their tendency to produce overconfident predictions even under high uncertainty, limits their reliability in safety-critical applications. In the clinical setting, minimizing the incorrect predictions is often more important than maximizing the overall coverage. This paper investigates the use of selective prediction via training dynamics (SPTD) to improve diagnostic reliability by enabling models to abstain from uncertain predictions. Unlike the conventional confidence based approaches, SPTD leverages prediction instability across training checkpoints as a proxy for uncertainty. Experiments are conducted on three different medical imaging datasets including diabetic retinopathy, urine cell classification and bone tumor classification datasets. Results clearly demonstrate that SPTD has consistently improve the prediction accuracy at lower coverage levels and achieved more than 20% improvement compared to non-SPTD model. The findings highlight the effectiveness of training dynamics as a robust uncertainty estimation mechanism and establish selective prediction as a viable strategy for reducing diagnostic errors in healthcare AI systems.
Keywords: Deep learning, Medical Imaging, Selective Prediction, Training Dynamics, Uncertainty Estimation, Abstention.