Optimized stacking ensemble for early-stage diabetes mellitus prediction

This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures

An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver

In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.Comment: 4 pages, 2 figures, edited version of published paper in IEEE ICITE 201

Biophysical and Biochemical Screening Approaches for Antimicrobial Drug Discovery Targeting S. aureus ClpP

The discovery of antibacterial drugs has been among most significant achievements of mankind in saving millions of lives across the planet from infectious diseases. With rise in resistance to almost all existing chemotypes, the design of next generation novel antibiotics has become much more challenging and difficult. The early 21st century witnessed the advancement of multiple novel chemotypes during golden age of antibiotics however the pace of antibiotic drug discovery has slowed down tremendously, contributing to life threatening antimicrobial discovery void since 1980’s. Therefore the need to develop novel antibiotics with unique mechanism of action to leverage against multi drug resistance pathogens, is paramount. In this direction the Caseinolytic Protease P (ClpP) is an emerging drug discovery target with significant potential for treatment of recalcitrant biofilm forming infections from pathogens such as Methicillin-resistant Staphylococcus aureus (MRSA) This dissertation highlights the ongoing efforts to facilitate the discovery of novel non peptidic ClpP activator compounds and improvement of pharmacological profile of existing ClpP targeting Acyldepsipeptides (ADEPs) series antibiotics. The chapter one discusses the history and synopsis of conventional antibiotics drug discovery screening approaches, and transitions to modern era structure or fragment based screening approaches. The merits and challenges of such approaches of targeting a well conserved bacterial protease (ClpP) are discussed along with dissertation aims toward development of biophysical and biochemical screening approaches. Chapter two discusses optimization of thermal shift assay as primary screening assay for ClpP and its utility toward screening of fragment collections and buffer conditions. Chapter three discussed the development of a site specific Fluorescence Polarization based FP probe based on ADEP scaffold and its utility as a robust high throughput capable primary screening assay for screening of diverse collections ranging from bioactives to fragments. Chapter four discusses development of a label free Surface Plasmon Resonance (SPR) based assay geared toward screening of fragment as well as in house small and large (ADEP analogs) series compounds in addition to determining full kinetics for lead prioritization. Chapter five discusses the results of multiple screening campaigns utilizing combination of above assays to generate multiple hits with superior ligand efficiency and chemical tractability. Chapter six concludes with analysis of the best of compounds among individual series or from screening campaigns and highlights effectiveness of above screening assays toward hit exploration along with outlook on anticipated challenges and future directions

A broadband bistable piezoelectric cantilever-based vibration energy harvester with nonlinear high power extraction

This work presents a nonlinear vibration energy harvester, which combines a nonlinear bistable broadband piezoelectric cantilever used to transduce ambient vibration energy, with synchronized capture for efficient harvesting over broadband sources. An accurate model of the bistable transducer, that augments the Butterworth van Dyke piezoelectric model to capture the external magnetic force added as a bias to the external vibrations, is presented. Its validity has been demonstrated through physical implementation and experimental validation against simulation of the mathematical model. For efficient extraction of the transduced energy, nonlinear extraction circuits, namely synchronous charge extraction (SCE) and parallel synchronized switch harvesting on inductor (SSHI), are employed. The switching in these circuits is implemented using a fully self-propelled, low-power electronic breaker circuit, capable of detecting extrema in the waveform to perform switching. Both simulated and experimental power outputs from the bistable harvester have been presented, with the SCE and parallel-SSHI providing average outputs with more than one-hundred (100) fold increase over the harvested power reported in literature for the same input, and further, even more significant gains are observed for broadband excitations. For the above mentioned harvester, bistability is introduced through the use of two repelling magnets, one mounted on the cantilever tip and the other at a fixed location opposite it. Excitations that can overcome the repulsive magnetic force cause the cantilever to snap between its two equilibrium states, increasing amplitude and velocity of vibration, resulting in higher power outputs. This improved performance is observed whenever the cantilever operates in the bistable mode. Lower-amplitude excitations are unable to overcome the repulsive force, causing the cantilever to vibrate around one of its equilibrium states, and with smaller amplitudes in the presence of the opposing repulsion. To circumvent this issue, the second part of the work presents a completely mechanical way of increasing the range of excitation amplitudes over which the system remains bistable, by spring-loading the previously fixed-positioned magnet, and restricting its motion in the horizontal direction, towards and away from the cantilever. Then, whenever the cantilever moves towards the spring-loaded magnet, the latter is pushed away due to the repulsive force, increasing the distance between the magnets, thereby reducing the repulsive force required to be overcome for bistable operation. The opposite occurs when the cantilever moves away. Thus, the role of spring-loading is to introduce a type of negative feedback, through the self-adjustment of the distance between the magnets, favoring bistable operation over a larger range of excitations, and this is accomplished without an added energy cost. A 90% gain in power output levels over the fixed magnet system was observed

Techniques for online analysis of large distributed data

With the advancement of technology, there has been an exponential growth in the volume of data that is continuously being generated by several applications in domains such as finance, networking, security. Examples of such continuously streaming data include internet traffic data, sensor readings, tweets, stock market data, telecommunication records. As a result, processing and analyzing data to derive useful insights from them in real time is becoming increasingly important. The goal of my research is to propose techniques to effectively find aggregates and patterns from massive distributed data stream in real time. In many real world applications, there may be specific user requirements for analyzing data. We consider three different user requirements for our work - Sliding window, Distributed data stream, and a Union of historical and streaming data. We aim to address the following problems in our research : First, we present a detailed experimental evaluation of streaming algorithms over sliding window for distinct counting, which is a fundamental aggregation problem widely applied in database query optimization and network monitoring. Next, we present the first communication-efficient distributed algorithm for tracking persistent items in a distributed data stream over both infinite and sliding window. We present theoretical analysis on communication cost and accuracy, and provide experimental results to validate the guarantees. Finally, we present the design and evaluation of a low cost algorithm that identifies quantiles from a union of historical and streaming data with improved accuracy

ANYWHERE AND EVERYWHERE: Third Cultured Kids and Their ‘Stabilizing Factors’

The purpose of this research is to explore the significance of the Third Culture Kid (TCK) and the impact their ‘stabilizing factors’ or ‘constants’ have on their occupation outcome. Third Culture Kid is a term used for people who have travelled and lived in many different countries during their development years. Within that definition there are other subgroups such as: Bi/multi-cultural and/or bi/multi-racial families, immigrants, international adoptees, refugees and Domestic TCKs or CCKs. Ever since the term was first coined by Ruth Hill Useem, a sociologist, in the early 50s, there have been more and more families having the TCK experience due to the world becoming more globalized. Stabilizing factors or constants is a state of mind where one feels connected and is a part of something bigger than he or she is. This is important to study because TCKs do not have a city, town, people whom have been there whilst they were growing up (apart from the parents) during their lifetime. The constants they form within their lifetime (friends, teachers, hobbies, different cultures) become that city, town or people that they did not have before. These constants eventually help pave way for their career choices. For this study there was inputs from seven TCKs, two non-TCKs, one CCK, three international educators, three governmental officials, and one former refugee. In a constantly changing environment TCKs need something to ground themselves. From the interviews and questionnaires conducted themes of TCKs, international education, friends, cultural assimilation/adjustment, ‘Constants’, and the job opportunities for TCKs came up. All the TCKs’ ‘stabilizing factors’ have some say on their job outcome, directly or indirectly. The most common ‘stabilizing factors’ which were found were the parents of TCKs