Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

Section I: Anatomy Surgical Anatomy of the Retroperitoneum, Kidneys, and Ureters Anatomy of the Lower Urinary Tract and Male Genitalia Section II: Clinical Decision Making Evaluation of the Urologic Patient: History, Physical Examination, and Urinalysis Urinary Tract Imaging: Basic Principles Outcomes Research Section III: Basics of Urologic Surgery Basic Instrumentation and Cystoscopy Basics of Laparoscopic Urologic Surgery Section IV: Infections and Inflammation Infections of the Urinary Tract-A. Schaeffer Inflammatory Conditions of the Male Genitourinary Tract Interstitial Cystitis and Related Disorders Sexually Transmitted and Associated Diseases Urological Implications of AIDS and Related Conditions Cutaneous Diseases of the External Genitalia Tuberculosis and Other Opportunistic Infections of the Genitourinary System Section V: Molecular and Cellular Biology Basic Principles of Immunology Molecular Genetics and Cancer Biology Tissue Engineering Perspectives for Reconstructive Surgery Section VI: Reproductive and Sexual Function Male Reproductive Physiology Male Infertility Surgical Management of Male Infertility Physiology of Erectile Dysfunction: Pathophysiology, Evaluation, Nonsurgical Management Epidemiology, Evaluation, and Nonsurgical Management of Erectile Dysfunction Prosthetic Surgery for Erectile Dysfunction Vascular Surgery for Erectile Dysfunction Peyronie's Disease Priapism Androgen Deficiency in the Aging Male Female Sexual Function and Dysfunction Section VII: Male Genitalia Neoplasms of the Testis Surgery of Testicular Tumors Tumors of the Penis Surgery of Penile and Urethral Carcinoma Surgery of the Penis and Urethra Surgery of the Scrotum and Seminal Vesicles Section VIII: Renal Physiology and Pathophysiology Renal Physiology and Pathophysiology Renovascular Hypertension Section IX: Upper Urinary Tract Obstruction and Trauma Pathophysiology of Obstruction Management of Upper Urinary Tract Obstruction Upper Urinary Tract Trauma Section X: Renal Failure and Transplantation Renal Transplantation Etiology, Pathogenesis, and Management of Renal Failure Section XI: Urinary Lithiasis and Endourology Urinary Lithiasis: Etiology, Epidemiology, and Pathophysiology Evaluation and Medical Management of Urinary Lithiasis Surgical Management of Upper Urinary Tract Calculi Ureteroscopy and Retrograde Ureteral Access Percutaneous Approaches to the Upper Urinary Tract Section XII: Neoplasms of the Upper Urinary Tract Renal Tumors Urothelial Tumors of the Upper Urinary Tract Urothelial Tumors of the Renal Pelvis and Ureter Open Surgery of the Kidney Laparoscopic Surgery of the Kidney Ablative Therapy for Renal Tumors Section XIII: The Adrenals Pathophysiology, Evaluation, and Medical Management of Adrenal Disorders Surgery of the Adrenals Section XIV: Urine Transport, Storage, and Emptying Physiology and Pharmacology of the Renal Pelvis and Ureter Physiology and Pharmacology of the Bladder and Urethra Pathophysiology, Categorization, and Management of Voiding Dysfunction Urodynamic and Video dynamic Evaluation of Voiding Dysfunction Neuromuscular Dysfunction of the Lower Urinary Tract Urinary Incontinence: Epidemiology, Pathophysiology, Evaluation, and Overview of Management The Overactive Bladder Pharmacologic Management of Storage and Emptying Failure Conservative Management of Urinary Incontinence: Behavioral and Pelvic Floor Therapy, Urethral and Pelvic Devices Electrical Stimulation and Neuromodulation in Storage and Emptying Failure Retropubic Suspension Surgery for Incontinence in Women Vaginal Reconstructive Surgery for Sphincteric Incontinence Pubovaginal Slings Tension-Free Vaginal Tape Procedures Injection Therapy for Urinary Incontinence Additional Treatment for Storage and Emptying Failure Geriatric Voiding Dysfunction and Urinary Incontinence Urinary Tract Fistulae Bladder and Urethral Diverticula Surgical Procedures for Sphincteric Incontinence in the Male: The Artificial Genitourinary Sphincter Perineal Sling Procedures Section XV: Bladder Lower Genitourinary Calculi and Trauma Urothelial Tumors of the Bladder Management of Superficial Bladder Cancer Management of Metastatic and Invasive Bladder Cancer Surgery of Bladder Cancer Laparoscopic Bladder Surgery Use of Intestinal Segments in Urinary Diversion Cutaneous Continent Urinary Diversion Orthotopic Urinary Diversion Genital and Lower Urinary Tract Trauma Lower Urinary Tract Calculi Section XVI: Prostate Molecular Biology, Endocrinology, and Physiology of the Prostate and Seminal Vesicles Etiology, Pathophysiology, and Epidemiology of Benign Prostatic Hyperplasia Natural History, Evaluation, and Nonsurgical Management of Benign Prostatic Hyperplasia Minimally Invasive and Endoscopic Management of Benign Prostatic Hyperplasia Retropubic and Superpubic Open Radical Prostatectomy Epidemiology, Etiology, and Prevention of Prostate Cancer Pathology of Prostatic Neoplasms Ultrasonography and Biopsy of the Prostate Tumor Markers in Prostate Cancer Early Detection, Diagnosis, and Staging of Prostate Cancer Definitive Therapy of Localized Prostate Cancer: Outcomes Expectant Management of Prostate Cancer Anatomic Retrograde Retropubic Prostatectomy Radical Perineal Prostatectomy Laparoscopic and Robotic Radical Prostatectomy and Pelvic Lymphadenectomy Radiation Therapy for Prostate Cancer Cryotherapy of Prostate Cancer Treatment of Locally Advanced Prostate Cancer Management of Rising Prostate-Specific Antigen after Definitive Therapy Hormonal Therapy for Prostate Cancer Management of Hormone-Resistant Prostate Cancer Section XVII: Pediatric Urology Normal and Anomalous Development of the Urinary Tract Renal Function in the Fetus Congenital Obstructive Uropathy Perinatal Urology Evaluation of Pediatric Urologic Patient Renal Disease in Childhood Urinary Tract Infections in Infants and Children Anomalies of the Kidney Renal Dysplasia and Cystic Disease of Kidney Anomalies and Surgery of the Ureteropelvic Junction Ectopic Ureter Vesicoureteral Reflux Prune-Belly Syndrome Exstrophy and Epispadias Complex Surgical Technique for One-Stage Exstrophy Reconstruction Bladder Anomalies in Children Posterior Urethral Valves and Other Urethral Anomalies Voiding Dysfunction in Children: Neurogenic and Non-neurogenic Urinary Tract Reconstruction Hypospadias Abnormalities of External Genitalia in Boys Abnormalities of Testis and Scrotum: Surgical Management Sexual Differentiation: Normal and Abnormal Surgical Management of Intersex Pediatric Oncology Pediatric Endourology and Laparoscopy Pediatric Genitourinary Trauma

Campbell-Walsh urology

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

BACKGROUND Semen quality is taken as a surrogate measure of male fecundity in clinical andrology, male fertility, reproductive toxicology, epidemiology and pregnancy risk assessments. Reference intervals for values of semen parameters from a fertile population could provide data from which prognosis of fertility or diagnosis of infertility can be extrapolated. METHODS Semen samples from over 4500 men in 14 countries on four continents were obtained from retrospective and prospective analyses on fertile men, men of unknown fertility status and men selected as normozoospermic. Men whose partners had a time-to-pregnancy (TTP) of < or =12 months were chosen as individuals to provide reference distributions for semen parameters. Distributions were also generated for a population assumed to represent the general population. RESULTS The following one-sided lower reference limits, the fifth centiles (with 95th percent confidence intervals), were generated from men whose partners had TTP < or = 12 months: semen volume, 1.5 ml (1.4-1.7); total sperm number, 39 million per ejaculate (33-46); sperm concentration, 15 million per ml (12-16); vitality, 58% live (55-63); progressive motility, 32% (31-34); total (progressive + non-progressive) motility, 40% (38-42); morphologically normal forms, 4.0% (3.0-4.0). Semen quality of the reference population was superior to that of the men from the general population and normozoospermic men. CONCLUSIONS The data represent sound reference distributions of semen characteristics of fertile men in a number of countries. They provide an appropriate tool in conjunction with clinical data to evaluate a patient's semen quality and prospects for fertility.

/pdf/world-health-organization-reference-values-for-human-semen-3qu21s6tqf.pdf

World Health Organization reference values for human semen characteristics

No AccessJournal of UrologyUrological Survey1 Nov 2023Male Infertility Craig NiederbergerMD Craig NiederbergerCraig Niederberger More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003672AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Male Infertility." The Journal of Urology, 210(5), pp. 810–811 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 210Issue 5November 2023Page: 810-811 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.Metrics Author Information Craig Niederberger More articles by this author Expand All Advertisement PDF downloadLoading ... 

Male Infertility.

Male infertility

Re: Associations of Sperm Mitochondrial DNA Copy Number and Deletion Rate with Fertilization and Embryo Development in a Clinical Setting.

Re: A single nucleotide polymorphism within the novel sex-linked testis-specific retrotransposed PGAM4 gene influences human male fertility.

Quality of cryopreserved testicular sperm in patients with obstructive and nonobstructive azoospermia.

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