Network Structures
A neural network structure has at least two physical components namely the processing elements and the connections between those physical components. The processing elements are called neurons and the connections between those neurons are called as links. Every link has the weight parameter associated with it. There are different ways in which the information can be processed by a neuron and different ways of connecting the neurons to one another. Different types of neural network structures can be constructed using different processing elements and by the specific manner in which they are connected. A variety of neural network structures has been developed for processing, pattern recognition, control and much more. The neural network structures include multi-layer perceptrons (MLP), radial basis function networks (RBF), wavelet neural networks, arbitrary structures, self-organizing maps (SOM) and recurrent networks.
Summary
A neural network structure has at least two physical components namely the processing elements and the connections between those physical components. The processing elements are called neurons and the connections between those neurons are called as links. Every link has the weight parameter associated with it. There are different ways in which the information can be processed by a neuron and different ways of connecting the neurons to one another. Different types of neural network structures can be constructed using different processing elements and by the specific manner in which they are connected. A variety of neural network structures has been developed for processing, pattern recognition, control and much more. The neural network structures include multi-layer perceptrons (MLP), radial basis function networks (RBF), wavelet neural networks, arbitrary structures, self-organizing maps (SOM) and recurrent networks.
Things to Remember
- A neural network structure has at least two physical components namely the processing elements and the connections between those physical components.
- The processing elements are called neurons and the connections between those neurons are called as links.
- Every link has the weight parameter associated with it. There are different ways in which the information can be processed by a neuron and different ways of connecting the neurons to one another.
- Different types of neural network structures can be constructed using different processing elements and by the specific manner in which they are connected.
- A variety of neural network structures has been developed for processing, pattern recognition, control and much more.
- The neural network structures include multi-layer perceptrons (MLP), radial basis function networks (RBF), wavelet neural networks, arbitrary structures, self-organizing maps (SOM) and recurrent networks.
MCQs
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Subjective Questions
Q1:
Define MVA .
Type: Very_short Difficulty: Easy
Q2:
What are the criteria and advantages of MVA ?
Type: Short Difficulty: Easy
<ol>
<li>Uterine size less than or equal to 12 weeks</li>
<li>Woman condition should be stable</li>
<li>Pulse less than 110/minutes</li>
<li>Hemoglobin is equal or moral than 7gm%</li>
<li>Per vaginal bleeding less than few days</li>
</ol>
<p> </p>
<h4>Advantages</h4>
<ol>
<li>Fewer chances of complication</li>
<li>Less expensive</li>
<li>Good and effective method of treatment</li>
<li>Easily available in both rural and urban areas</li>
</ol>
Q3:
What are the precaution prior to performing MVA?
Type: Long Difficulty: Easy
<p>Special precautions should be taken when the uterine size is differently determined by the pelvic examination to differ greatly from that determine by the last menstrual period or by the uterine size beyond the 1st trimester.</p>
<ol>
<li><strong>Initial assessment</strong></li>
</ol>
<p>- Greeting the woman respectfully.</p>
<p>- Ensuring privacy</p>
<p>- Assess the woman condition for shock and other life-threatening conditions.</p>
<p>- In a case of any complication stabilized the patient and refer for the immediate treatment according to woman’s conditions.</p>
<p>- Check the woman’s vital signs, bleeding, any injury, and then stabilized her and refer.</p>
<p> </p>
<ol start="2">
<li><strong>Medical evaluation</strong></li>
</ol>
<p>- Reproductive history taking</p>
<p>- Physical and pelvic examination</p>
<p>- Laboratory tests and investigation</p>
<p>- Proper counselling about the woman condition</p>
<p>- Complete and appropriate counselling about the use of family planning</p>
<p> </p>
<ol start="3">
<li><strong>Preparation for procedure</strong></li>
</ol>
<p>- Proper counselling and educating the woman about the procedure before starting in a polite manner and friendly.</p>
<p>- Emotional support, comfort by taking with her during a procedure.</p>
<p>- Inform about all the discomfort during procedure</p>
<p>- Give the PCM 500mg orally before starting the procedure</p>
<p>- Ask for any allergic reaction to the medicines</p>
<p> </p>
<p>- Maintain proper hygiene and aseptic technique throughout the procedure by:</p>
<ol>
<li>Proper and thoroughly hand washing with soap and water</li>
<li>Use of sterile and highly disinfected instrument and gloves.</li>
<li>Cleaning the cervix and vagina with the aseptic technique before inserting anything’s per vagina or cervix and uterine cavity.</li>
</ol>
<p> </p>
Q4:
List the complication of MVA ?
Type: Short Difficulty: Easy
<li>Incomplete evacuation</li>
<li>Uterine perforation</li>
<li>Cervical laceration</li>
<li>Pelvic inflammation</li>
<li>Haemorrhage, Shock</li>
<li>Severe vaginal bleeding</li>
<li>Air embolism</li>
<li>Rupture of the uterus</li>
<li>Dysmaturity</li>
<li>Preterm labour, Ectopic pregnancy</li>
</ol>
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Network Structures
Network Structures
A neural network structure has at least two physical components namely the processing elements and the connections between those physical components. The processing elements are called neurons and the connections between those neurons are called as links. Every link has the weight parameter associated with it. Each neuron receives stimulus from the neighboring neurons connected to it and processes the information in order to produce an output. The neurons that receive stimuli from outside the network that are not from the neurons of the network are called input neurons. The neurons whose outputs are used externally are called output neurons. The neurons that receive stimuli from other neurons whose output is a stimulus for other neurons in the neural network are known as hidden neurons. There are different ways in which the information can be processed by a neuron and different ways of connecting the neurons to one another.
Different types of neural network structures can be constructed using different processing elements and by the specific manner in which they are connected.
A variety of neural network structures has been developed for processing, pattern recognition, control and much more. The neural network structures include multi-layer perceptrons (MLP), radial basis function networks (RBF), wavelet neural networks, arbitrary structures, self-organizing maps (SOM) and recurrent networks.
References:
- Elaine Rich, Kevin Knight 1991, "Artificial Intelligence".
- Nilsson, Nils J. Principles of Artificial Intelligence, Narosa Publishing House New Delhi, 1998.
- Norvig, Peter & Russel, Stuart Artificial Intelligence: A modern Approach, Prentice Hall, NJ, 1995
- Patterson, Dan W. Introduction to Artificial Intelligence and Expert Systems, Prentice Hall of India Private Limited New Delhi, 1998.
Lesson
Applications of AI
Subject
Computer Engineering
Grade
Engineering
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