Preface
This book is organized into fourteen chapters. Chapter 1 begins by introducing the concept of bio-inspired algorithms and their underlying principles. It then explores the opportunities offered by these algorithms, such as their capacity to locate the best answers in very big and intricate search fields, their robustness in dealing with uncertainty and noise, and their potential for parallel and distributed computing. The chapter will also highlight the application areas where bio-inspired algorithms have shown promising results, including in optimization problems, pattern recognition, and swarm robotics.
In Chapter 2, the semipermeable membrane of an egg was separated and used to assess the Mace spice's in vitro antiurolithiatic efficacy. By performing in vitro urolithiasis on the chosen plant, Mace spice, and using the common medication Neeri, the percentage of kidney stone dissolving was discovered. The Mace spice's ethanol leaf extracts have the greatest medication dissolving rates. Thus, through our research, it has been established that the Mace spice plant contains antiurolithiatic properties.
In Chapter 3, Parkinson's disease (PD) is normally diagnosed in a person after a thorough physical examination by a doctor that considers the patient's medical past, neurological examination, evaluation of motor symptoms, and other supporting tests with precise diagnostic criteria. However, there is no surefire way to identify PD. Furthermore, other medical problems, including arthritis and stroke, should be evaluated on subsequent visits because they can show similarly to Parkinson's disease. Only 80.6% of PD diagnoses are correct overall. Machine learning techniques can be applied in a variety of ways to develop different methods to recognize the presence of PD in an individual, as well as identify it in the initial stages of the disease, to aid doctors in the diagnosis of PD.
Chapter 4 explains the importance of detecting the tumors immediately. Various mechanical methods exist for the detection of tumors, such as Magnetic Resonance Imaging (MRI), Computerized Tomography (CT) Scan, and Positron Emission Tomography (PET) Scan. These scans send the waves into the patient's body to obtain images of the organ or body part affected. Images obtained from these scans are of Digital Imaging and Communications in Medicine (DICOM) format. Different techniques are available for the classification of the tumor, such as segmentation, feature extraction, machine learning techniques, and deep learning models. These techniques are discussed and compared in this chapter, which will help researchers to find the optimal solution for their research in the detection and classification of different tumors and other deadly diseases. Finally, this chapter chronicles all the relevant and important literature related with tumor detection and how it can be classified using new age computer- based technology, such as deep learning and machine learning.
Chapter 5 addresses current developments in molecular subtyping and how they could enhance the precision of tumor categorization. The clinical ramifications of tumor detection and categorization, including diagnosis, prognosis, and treatment planning, are covered in the chapter's concluding section. In order to inform treatment choices, enhance patient outcomes, and save healthcare costs, this section emphasizes the significance of precise tumor diagnosis and classification. Healthcare workers, researchers, and students with an interest in tumor detection and treatment will find the chapter to be an invaluable resource.
In Chapter 6, researchers assessed the performance of these algorithms using a variety of evaluation criteria, including accuracy, precision, recall, and F1 score. Our findings show that, with an accuracy of 90.5%, DNNs outperformed the competition, followed by SVM (85.3%), RF (81.9%), and KNN (78.5%). In order to determine which aspects were most crucial for each algorithm, feature selection was also performed. Various algorithms were discovered that favored various feature sets. This research sheds light on the usefulness of various machine learning algorithms for classifying brain tumors, which will help in the creation of more precise and effective diagnostic equipment for this serious medical disease.
In Chapter 7, a series of mathematical equations simulate dingo behavior in the wild to find the best solutions to a variety of optimization challenges. The dingo optimizer has demonstrated promising results in several applications, including engineering design, financial forecasting, and bioinformatics. The algorithm is rather simple to implement and has the potential to be a useful tool for researchers and practitioners in a wide range of domains.
Chapter 8 discusses how genetic algorithms can be applied to the field of medicine. In addition to pharmacotherapy, other fields such as radiology, surgery, cardiology radiotherapy, pediatrics, endocrinology, obstetrics and gynaecology, pulmonary medicine, orthopaedics, rehabilitation medicine, neurology, infectious diseases, and health care administration have all found promising applications for the genetic algorithm. This chapter explains the genetic algorithm and how it may be used in healthcare administration, disease detection, treatment planning, drug safety monitoring, prognosis, and more. It also aids doctors in envisioning the future applications of this metaheuristic technique in healthcare.
Chapter 9 focuses on compounds that may comprise naturally occurring proteins or carbohydrates in our bodies. The immune system doesn't target these coated nanoparticles because it doesn't recognize them as being foreign when it sees them. Making the nanoparticles small enough to get past the body's natural defenses is another option. Small particles are harder for our immune system to find and recognize. The nanoparticles can therefore avoid detection and enter their target cells if we make them small enough. A computational method called an artificial immune system (AIS) algorithm can be used to create nanoparticles that bypass the immune system and deliver medications to specific cells. The AIS algorithm is inspired by the natural immune system. It is possible to create nanoparticles that can bypass the immune system and deliver medications to specific cells by using an artificial immune system algorithm.
In Chapter 10, the reader will learn about a novel ensemble approach that was developed for accurate blood vessel segmentation and classification. The retinal images had been accumulated from Digital Retinal Images for Vessel Extraction (DRIVE) and Structured Analysis of the Retina (STARE) datasets. The ensemble approach can segment the retinal blood vessels using bio-inspired algorithms and Cuttlefish Algorithm (CFA) for segmentation of the fundus image. The features are then extracted from the segmented pictures using the Enhanced Local Binary Pattern (ELBP) and Inverse Difference Moment Normalized (IDMN) algorithms.
Chapter 11 demonstrates how to predict behaviors and events thanks to data-driven linkages and patterns. Predictive analytics provides a look into the future by using past events. It is important to note that the predictive model was built using previous predictive approaches, even if it is not based on the production of a mathematical model or algorithms for the development of the forecast. Instead, it uses algorithms that are built into the identified tool. It is suggested that, via the usage of the model, the organizations that provide both public and private health services adopt it in a commercial setting, using the model's predictive capabilities for the client's diagnosis and the optimization of consultation procedures.
Chapter 12 focuses on neurological disease diagnosis, providing an overview of common diseases and current diagnostic techniques. It reviews traditional diagnostic methods and their limitations, highlighting the need for alternative approaches. The application of bio-inspired algorithms in neurological disease diagnosis is discussed in detail. Genetic algorithms can optimize feature selection and classification algorithms, as demonstrated through case studies and research findings. Neural networks are explored for their potential in improving disease diagnosis accuracy through bio-inspired optimization techniques. Other bio-inspired algorithms, such as ant colony optimization and particle swarm optimization, are also examined for their applications in feature selection, clustering analysis, and diagnostic model optimization. Finally, the chapter addresses the challenges and limitations associated with the use of bio-inspired algorithms in neurological disease diagnosis. It emphasizes the need for further research and development in this emerging field. Overall, bio-inspired algorithms hold great promise in advancing the field of neurological disease diagnosis, offering new opportunities for accuracy and efficiency.
As shown in Chapter 13, GA synergizes well with NNs to optimize models and approximate parameters, thereby enhancing the effectiveness of NNs. GA can be applied in various ways to design the best ANN for a given problem domain, including optimizing weights, selecting topology, choosing features, training, and improving interpretation. In the sections herein, several studies are presented that utilize different ANN optimization techniques through GA, depending on the research objectives.
Chapter 14 presents a modern idea that employs artificial intelligence and machine learning with a blend of bio-inspired methods. The...
