Logistic random forest boosting technique for Alzheimer's diagnosis.

Alzheimer's disease (AD) is a common and well-known neurodegenerative condition that causes cognitive impairment. In the field of medicine, it is the "nervous system" disorder that has received the most attention. Despite this extensive research, there is no treatment or strategy to slow or stop its spread. Nevertheless, there are a variety of options (medication and non-medication alternatives) that may aid in the treatment of AD symptoms at their various phases, thereby enhancing the patient's quality of life. As AD advances over time, it is necessary to treat patients at their various stages appropriately. As a result, detecting and classifying AD phases prior to symptom treatment can be beneficial. Approximately twenty years ago, the rate of progress in the field of machine learning (ML) accelerated dramatically. Using ML methods, this study focuses on early AD identification. The "Alzheimer's Disease Neuroimaging Initiative" (ADNI) dataset was subjected to exhaustive testing for AD identification. The purpose was to classify the dataset into three groups: AD, "Cognitive Normal" (CN), and "Late Mild Cognitive Impairment" (LMCI). In this paper, we present the ensemble model Logistic Random Forest Boosting (LRFB), representing the ensemble of "Logistic Regression" (LR), "Random Forest" (RF), and "Gradient Boost" (GB). The proposed LRFB outperformed LR, RF, GB, "k-Nearest Neighbour" (k-NN), "Multi-Layer Perceptron" (MLP), "Support Vector Machine" (SVM), "AdaBoost" (AB), "Naïve Bayes" (NB), "XGBoost" (XGB), "Decision Tree" (DT), and other ensemble ML models with respect to the performance metrics "Accuracy" (Acc), "Recall" (Rec), "Precision" (Prec), and "F1-Score" (FS).

Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review.

"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.

ICT Enabled Disease Diagnosis, Treatment and Management-A Holistic Cost-Effective Approach Through Data Management and Analysis in UAE and India.

This concept paper addresses specific challenges identified in the UN 2030 Agenda Sustainable Development Goals (SDG) as well as the National Health Policy of India (NHP-India) and the Ministry of Health Policy of UAE (MHP-UAE). This policy calls for a digital health technology ecosystem. SDG Goal 1 and its related objectives are conceptualized which serves as the foundation for Virtual Consultations, Tele-pharmacy, Virtual Storage, and Virtual Community (VCom). SDG Goals 2 and 3 are conceptualized as Data Management & Analytical (DMA) Architecture. Individual researchers and health care professionals in India and the UAE can use DMA to uncover and harness PHC and POC data into practical insights. In addition, the DMA would provide a set of core tools for cross-network initiatives, allowing researchers and other users to compare their data with DMA data. In rural, urban, and remote populations of the UAE and India, the concept augments the PHC system with ICT-based interventions. The ICT-based interventions may improve patient health outcomes. The open and flexible design allows users to access various digital materials. Extendable data/metadata format, scalable architecture for petabyte-scale federated discovery. The modular DMA is designed using existing technology and resources. Public health functions include population health assessment, policy development, and monitoring policy implementation. PHC and POC periodically conduct syndromic surveillance to identify population risk patterns. In addition, the PHC and POC deploy medical and non-medical preventive measures to prevent disease outbreaks. To assess the impact of social and economic factors on health, epidemiologists must first understand diseases. Improved health due to compliance with holistic disease treatment plans and access to scientific health information.

Auger electron emission initiated by the creation of valence-band holes in graphene by positron annihilation.

Auger processes involving the filling of holes in the valence band are thought to make important contributions to the low-energy photoelectron and secondary electron spectrum from many solids. However, measurements of the energy spectrum and the efficiency with which electrons are emitted in this process remain elusive due to a large unrelated background resulting from primary beam-induced secondary electrons. Here, we report the direct measurement of the energy spectra of electrons emitted from single layer graphene as a result of the decay of deep holes in the valence band. These measurements were made possible by eliminating competing backgrounds by employing low-energy positrons (<1.25 eV) to create valence-band holes by annihilation. Our experimental results, supported by theoretical calculations, indicate that between 80 and 100% of the deep valence-band holes in graphene are filled via an Auger transition.

Time of flight spectrometer for background-free positron annihilation induced Auger electron spectroscopy.

We describe a novel spectrometer designed for positron annihilation induced Auger electron spectroscopy employing a time-of-flight spectrometer. The spectrometer's new configuration enables us to implant monoenergetic positrons with kinetic energies as low as 1.5 eV on the sample while simultaneously allowing for the detection of electrons emitted from the sample surface at kinetic energies ranging from ∼500 eV to 0 eV. The spectrometer's unique characteristics made it possible to perform (a) first experiments demonstrating the direct transition of a positron from an unbound scattering state to a bound surface state and (b) the first experiments demonstrating that Auger electron spectra can be obtained down to 0 eV without the beam induced secondary electron background obscuring the low energy part of the spectra. Data are presented which show alternative means of estimating positron surface state binding energy and background-free Auger spectra.