Mariya Anto Bennet

Arrhythmia classification is categorization of irregular heart rhythms depending on patterns detected in electrocardiogram (ECG) signals assist in treatment and diagnosis of cardiac conditions. ECG evaluates heart’s electrical activity to diagnose various heart conditions, but it is affected by interference or noise. ECG’s signal filtering is essential pre-processing stage that minimizes noise and highlights wave characteristics in ECG data. However, digital filters are normally constructed by multiplying coefficient and then multiplying value given as feedback which leads to more power and area consumption. To solve these issues, coefficient memory compression (CMC) technique is proposed with an adaptive FIR filter (AFF) to achieve low area and low power dissipation by compressing memory requirements in a field programmable gate array (FPGA). An adaptive FIR filter is employed to effectively minimize noise like baseline noise, muscle contraction noise, and low-frequency noise. The performance of CMC-AFF is analyzed in terms of look up table (LUT), register, digital signal processing (DSP), power, and global buffer (BufG). The proposed approach achieves a low power consumption of 0.012 W in Zed Board Zynq7000 AP system on chip (SoC) FPGA device compared to existing techniques like collateral and sequence approaches using Bartlet filter and low-power ECG processor using Bartlet filter respectively.

From smart home devices to wearable devices, electronics have become an indispensable part of modern life. Vast volumes of data have been collected by these electronic devices, revealing precise information about device communications, user behaviours, and more. Improvements to device features, insights into the user experience, and the detection of security risks are just some of the many uses for this information. However, advanced analytical methods are required to make sense of this plethora of data successfully. The K-means clustering algorithm is used in the present research to analyse the data sent and received by different types of electronics. The first step of the research is collecting data, intending to create a representative sample of people using various devices and communication methods. After collecting data, preprocessing is necessary to ensure it can be analysed successfully. In the next step, the K-means algorithm classifies the information into subsets that stand for distinct modes of interaction. The primary objective of the research is to gain an improved understanding of these groups by demonstrating how users communicate, device communication, and possibilities for enhancing functionality and security.

Alzheimer’s disease (AD) is the most common form of dementia in senior individuals. It is a progressive neurological ailment that predominantly affects memory, cognition, and behavior. An early AD diagnosis is essential for effective disease management and timely intervention. Due to its complexity and heterogeneity, AD is, however, difficult to diagnose precisely. This paper investigates the integration of disparate machine learning algorithms to improve AD diagnostic accuracy. The used dataset includes instances with missing values, which are effectively managed by employing appropriate imputation techniques. Several feature selection algorithms are applied to the dataset to determine the most relevant characteristics. Moreover, the Synthetic Minority Oversampling Technique (SMOTE) is employed to address class imbalance issues. The proposed system employs an Ensemble Classification algorithm, which integrates the outcomes of multiple predictive models to enhance diagnostic accuracy. The proposed method has superior disease prediction capabilities in comparison to existing methods. The experiment employs a robust AD dataset from the UCI machine learning repository. The findings of this study contribute significantly to the field of AD diagnoses and pave the way for more precise and efficient early detection strategies.