Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning

Cardiovascular diseases (CVD) have recently outdid all other reasons of death universal in both developing and developed nations. Initial detection of cardiac conditions and continuing therapeutic supervision by experts can lower the death rate. However, accurate diagnosis of cardiac issues in all circumstances and 24-hour patient consultation by a doctor are still not feasible due to the increased intellect, effort, and expertise required. In this study, a basic concept for an Machine Learning (ML)-based heart disease prediction system was presented to identify impending heart disease using Machine Learning techniques. Despite the increasing number of empirical studies in this topic, particularly from underdeveloped countries, here lack many synthesised research articles in the field. In a time when the amount of data available is constantly increasing, predictive analytics has become more and more important as a tool for heart welfare services and human protection.  By utilising data collected from previous events to predict future patterns and outcomes, this state-of-the-art technology assists heart-care agencies in making more informed decisions about how to best serve their clients. However, as with any other data-driven technology, predictive analytics must be used appropriately to guarantee effective and ethical business operations. Healthcare forecasting has gained importance in recent years due to the growing popularity of AI (Artificial Intelligence) and ML (Machine Learning). In the healthcare sector, forecasting can also aid physicians in providing more precise and timely diagnoses. By anticipating likely medical events, medical staff can identify and treat individuals with greater efficiency and precision. This could lead to better patient outcomes and even cost savings.  These systems provide excellent therapeutic support and have the ability to diagnose illnesses by mimicking human cognition.  This study's included studies focus on forecasting the heart healthcare system (HHS) using machine learning algorithms. We implemented the system using the K-means Elbow technique for registration and notification, a decision tree for HHS, and MySQL for immunisation reminders

Recent advances in IoT, AI, and national technology resilience

Internet of Things (IoT) and Artificial Intelligence (AI) are the critical enablers of the Industrial Revolution 4.0. IoT can be used in many applications that require precision, such as agriculture, industrial automation, education, automotive, and smart cities, to name a few. In other words, IoT is a powerful technology that can solve various business problems. Nevertheless, its integration with AI can help to take automation to the next level. This talk aims to discuss the recent advances in IoT, edge computing, and its applications. First, the IoT and edge commercial adoption survey 2021 will be highlighted. Then, the IoT framework will be introduced to solve a complex problem, including Things, Connect, Collect, Learn, and Do. Especially, the Learn part is very much related to AI. Then, some applications using IoT and edge computing will be presented. Finally, national technology resilience is now a necessity rather than necessary due to the current world situation. Therefore, future directions to enhance national technology resilience will be elaborated

Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion

This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecting biometric system accuracy and po-tential improvements. Our analysis delves into physiological and behavioral traits, exploring their pros and cons. We discuss factors influencing biometric system effectiveness and highlight areas for enhancement. Our study differs from previous surveys by extensively examining biometric traits, exploring various application domains, and analyzing measures to mitigate cyberattacks. This paper aims to inform researchers and practitioners about the biometric authentication landscape and guide future advancements

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper

SMART TECHNIQUES FOR FAST MEDICAL IMAGE ANALYSIS AND PROCESSING

Medical Imaging has become an important transversal applications and re- search field that embraces a great variety of sciences. Imaging is the central science of measurement in diagnosis and treating diseases. The effort of the technological progress has made possible human imaging starting from a single molecule to the whole body. The open challenge is to treat the huge amount of medical informations with the use of smart and fast techniques that allows clinical and images data analysis and processing. In this ph.D. Thesis, many issues have been addressed and a certain amount of improvement in various fields have been produced, such as biom- etry, organs and tissues segmentation, MRI thermometry, medical reports retrieval and classification. The topic prefixed at the beginning of this ph.D. route was to analyze, understand, and give a step over to various kind of problematics related to Medical Images and Data analysis, working closely to radiologist physicians, with specific equipments, and following the common denominator of fast and smart methodologies applied to the medical imaging issue. A series of contribution have been carried out in fields such as: • proposing two different kind of multimodal biometric authentication systems that investigates fingerprint and iris fusion and processing; • applying expert systems to the issue of data validation, comparing and validating data to two different methodologies that assess liver iron overload in thalassemic patients;• addressing and improving non-invasive referenceless thermometry by using Radial Basis Function as interpolator; • applying the multi-seed region growing method to the segmentation of CT liver dataset; • proposing a novel unsupervised voxel-based morphology method for MRI brain segmentation by using k-means clustering and neural net- work classification; • proposing a novel ontology-based algorithm for information retrieval from mammographic text reports. The above work has been developed with the cooperation of the medical staff of the “Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi” and the “Scuola di Specializzazione in Radiodiagnostica" of the Università degli Studi di Palermo. All the proposed contributions show good performance using the stan- dard metrics. Most of them have produced scientific publications in com- puter science venues as well as in radiological venues. In addition, some specific frameworks, such as OsiriX, have been used to improve usability and easiness of the developed systems

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research