Comparing diagnosis of depression in depressed patients by EEG, based on two algorithms :Artificial Nerve Networks and Neuro-Fuzy Networks

Background and aims: Depression disorder is one of the most common diseases, but the diagnosis is widely complicated and controversial because of interventions, overlapping and confusing nature of the disease. So, keeping previous patients’ profile seems effective for diagnosis and treatment of present patients. Use of this memory is latent in synthetic neuro-fuzzy algorithm. Present article introduces two neuro-fuzzy and artificial neural network algorithms as an aid for psychologists and psychiatrists to diagnose and treat depression. Methods: Neuro-fuzzy has been carried out using data evaluated by psychiatrists and scholars in Tabriz city with the convenience sampling method. Sixty-five patients were studied from whom 40 patients were taught feed forward, back propagation by artificial neural network algorithm and 14 patients were tested. An inductive neuro-fuzzy intervention created neuro-fuzzy rules to decide about depression diagnosis. Results: The proposed neuro-fuzzy model created better classifications. Reaching maximum accuracy of 13.97is appropriate in diagnosis prediction. The results of the present study indicated that neuro-fuzzy is more powerful than artificial neural network with accuracy 76.88. Conclusion: Findings of the research showed the depression scores of beck inventory can be predicted and explained with the accuracy of 87 using EEG in F4 and alpha peak frequency. It can be said that such accuracy in predicting can’t be obtained by any regression or route analysis method. The research can be the first step to predict and even identify depression using taking the data directly from the brain. So, there is no need for inventory and even a specialist diagnosis

Electroencephalogram Signalling diagnosis using Softcomputing

The two most frightening things for the researchers in clinical signal processing and computer aided diagnosis are noise and relativity of human judgment. The researchers made effort to overcome these two challenges by using various soft computing approaches. In this article the present benefits of these approaches in the accomplishment of the analysis of electroencephalogram (EEG) is acknowledge. There is also the presentation of the significance of several trend and prospects of further softcomputing methods that can produce better results in signal processing of EEG. Medical experts apply the different softcomputing techniques for disease diagnoses and decision making systems performed on brain actions and modeling of neural impulses of the human encephalon

Computational intelligence techniques in medicine

El advenimiento de la Era de la Información, también conocida como la Era Digital, ha realizado un profundo impacto en las ciencias de la salud. Vastas cantidades de conjuntos de datos fluyen ahora a través de los diferentes estratos de las organizaciones sanitarias, y existe un requisito importante para extraer el conocimiento y emplearlo en la mejora de estos centros en todos los aspectos. Los sistemas informáticos inteligentes proporcionan apoyo a los profesionales de la salud implicados tanto en los contextos médicos como administrativos. Entre estos sistemas, métodos de inteligencia computacional han adquirido una creciente popularidad, dada su capacidad para hacer frente a grandes cantidades de datos clínicos e información precisa.El fin de esta edición especial es ofrecer una amplia visión de este apasionante campo, cuya creciente importancia es impulsada por el aumento de la disponibilidad de datos y sus potenciales de cálculo. The advent of the information age, also commonly known as the digital age, has made a profound impact on health sciences. Vast amounts of datasets now flow through the different stages of healthcare organizations, and there is a major requirement to extract knowledge and employ it to improve these centres in all respects. Intelligent computer systems provide support to health professionals involved both in the medical and managerial contexts. Amongst these systems, computational intelligence approaches have gained increasing popularity given their ability to cope with large amounts of clinical data and uncertain information. Thegoal of this special issue is to offer a broad view of this exciting field, the ever-growing importance of which is driven by the increasing availability of data and computational power.peerReviewe

A Neuro-Fussy Based Model for Diagnosis of Monkeypox Diseases

The largest vertebrate viruses known, infecting humans, and other vertebrates are poxviruses including cowpox, vaccinia, variola (smallpox), and monkeypox viruses. Monkeypox was limited to the rain forests of central and western Africa until 2003. A smallpox-like viral infection caused by a virus of zoonotic origin, monkeypox belongs to the genus Orthopoxvirus, family Poxviridae, and sub-family Chordopoxvirinae. Monkeypox has a clinical presentation like ordinary forms of smallpox, including flulike symptoms, fever, malaise, back pain, headache, and characteristic rash. In view of the eradication of smallpox, such symptoms in a monkepox endemic region should be carefully diagnosed. The problem in diagnosing monkeypox lies in the fact that it is clinically indistinguishable from other pox-like illnesses making virus differentiation difficult. In this paper, we present a neuro-fuzzy based model for early diagnosis of monkeypox virus with a differentiation from other pox families

Quality-by-design approach for the development of lipid-based nanosystems for anti-mycobacterial therapy

In this work, we rationally developed a lipid-based nanotechnological platform for hydrophobic anti-mycobacterial drugs. For this purpose, Artificial Intelligence tools were employed to assist formulation development, from the initial design to its conversion into a solid dosage form. Reproducible nanocarriers exhibiting suitable properties were achieved through a simple and robust procedure. Furthermore, the analysis of their in vitro performance revealed promising results in terms of permeability, cell uptake and selective intracellular release. Thus demonstrating the potential of these nanosystems to treat intestinal intracellular infections, increasingly related with Crohn´s disease development

Sistemas granulares evolutivos

Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia Elétric