ITS Teaching ASP Dot Net

Abstract: ASP dot net is one of the most widely used languages in web developing of its many advantages, so there are many lessons that explain its basics, so it should be an intelligent tutoring system that offers lessons and exercises for this language.why tutoring system? Simply because it is one-one teacher, adapts with all the individual differences of students, begins gradually with students from easier to harder level, save time for teacher and student, the student is not ashamed to make mistakes, and more. Therefore, in this paper, we describe the design of an Intelligent Tutoring System for teaching ASP dot net to help students learn ASP dot net easily and smoothly. Tutor provides beginner level in ASP dot net. Finally, we evaluated our tutor and the results were excellent by students and teacher

ARDUINO Tutor: An Intelligent Tutoring System for Training on ARDUINO

This paper aims at helping trainees to overcome the difficulties they face when dealing with Arduino platform by describing the design of a desktop based intelligent tutoring system. The main idea of this system is a systematic introduction into the concept of Arduino platform. The system shows the circuit boards of Arduino that can be purchased at low cost or assembled from freely-available plans; and an open-source development environment and library for writing code to control the board topic of Arduino platform. The system is adaptive with the trainee’s individual progress. The system functions as a special tutor who deals with trainees according to their levels and skills. Evaluation of the system has been applied on professional and unprofessional trainees in this field and the results were good

Knowledge Based System for Long-term Abdominal Pain (Stomach Pain) Diagnosis and Treatment

Abstract: Background: the abdomen is called (the belly, tummy, stomach, or midriff) establishes the part of the body between the thorax (chest) and pelvis, in humans. The abdomen contains most of the tube like organs of the digestive tract, as well as several solid organs. Hollow abdominal organs comprise the stomach, the small intestine, and the colon with its attached appendix. Organs such as the liver, its attached gallbladder, and the pancreas function in close association with the digestive tract and communicate with it via ducts. Objectives: the main goal of this expert system is to get the appropriate diagnosis of abdomen disease and the correct treatment. Methods: in this paper the design of the proposed expert system which was produced to help internist physicians in diagnosing many of the abdomen diseases such as: hiatal hernia, gastritis, ulcer or heartburn; the proposed expert system presents an overview about abdomen diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. Clips expert system language was used for designing and implementing the proposed expert system. Results: the proposed abdomen diseases diagnosis expert system was evaluated by medical students and they were satisfied with its performance. Conclusions: the proposed expert system is very useful for internist physician, patients with abdomen problem and newly graduated physician

Biosupercapacitors for Implantable Bioelectronics & Portable Microfluidic Devices for Prostate Cancer Biomarker Detection and DNA Damage Screening

Cardiovascular diseases and cancer are the top two leading causes of death in the United States according to the U.S. department of health and human services. Fast growing technologies are being developed to early diagnose and/or treat both heart diseases and cancers. One of the most successful cardiovascular devices is the cardiac pacemaker. Although cardiac pacemakers have been used by millions of patients worldwide, these pacemakers still suffer from several limitations because of the power source. Like other electronic devices that rely on batteries for their power, cardiac pacemakers must be replaced when the battery is drained. In addition, batteries electrode materials and electrolytes are toxic which raise serious safety concerns if leakage happen inside the patient’s body. In addition to their toxicity, these batteries represent more than 50-70 % of the size of implantable pacemakers which limits further miniaturization. In addition, portable electrochemical biosensors require durable portable power source to drive their electrochemical reaction and obtain the detection signal. In this thesis, biosupercapacitors were first developed as thin, safe, light-weight, low-cost, and durable power sources for the next generation of miniaturized implantable biomedical devices and portable disease biosensors. Moreover, additive manufacturing techniques such as 3-D printing, and screen printing were used to fabricate the different components of the portable electrochemical biosensors designed for cancer biomarker detection as well as DNA damage screening assays. Finally, novel triboelectric nanogenerator devices were developed and used as a sensor/energy harvester systems for biomedical, mechanical, and soft robotics applications

Rip current : a potential hazard zones detection in Saint Martin's Island using Machine Learning Approach

Beach hazards would be any occurrences potentially endanger individuals as well as their activity. Rip current, or reverse current of the sea, is a type of wave that pushes against the shore and moves in the opposite direction, that is, towards the deep sea. The management of access to the beach sometimes accidentally push unwary beachgoers forward into rip-prone regions, increasing the probability of a drowning on that beach. The research suggests an approach for something like the automatic detection of rip currents with waves crashing based on convolutional neural networks (CNN) and machine learning algorithms (MLAs) for classification. Several individuals are unable to identify rip currents in order to prevent them. In addition, the absence of evidence to aid in training and validating hazardous systems hinders attempts to predict rip currents. Security cameras and mobile phones have still images of something like the shore pervasive and represent a possible cause of rip current measurements and management to handle this hazards accordingly. This work deals with developing detection systems from still beach images, bathymetric images, and beach parameters using CNN and MLAs. The detection model based on CNN for the input features of beach images and bathymetric images has been implemented. MLAs have been applied to detect rip currents based on beach parameters. When compared to other detection models, bathymetric image-based detection models have significantly higher accuracy and precision. The VGG16 model of CNN shows maximum accuracy of 91.13% (Recall = 0.94, F1-score = 0.87) for beach images. For the bathymetric images, the highest performance has been found with an accuracy of 96.89% (Recall= 0.97, F1-score=0.92) for the DenseNet model of CNN. The MLA-based model shows an accuracy of 86.98% (Recall=0.89, F1-score= 0.90) for random forest classifier. Once we know about the potential zone of rip current continuosly generating rip current, then the coastal region can be managed accordingly to prevent the accidents occured due to this coastal hazards

Rip Current: A Potential Hazard Zones Detection in Saint Martin’s Island using Machine Learning Approach

Beach hazards would be any occurrences potentially endanger individuals aswell as their activity. Rip current, or reverse current of the sea, is a typeof wave that pushes against the shore and moves in the opposite direction,that is, towards the deep sea. The management of access to the beach sometimes accidentally push unwary beachgoers forward into rip-prone regions,increasing the probability of a drowning on that beach. The research suggestsan approach for something like the automatic detection of rip currents withwaves crashing based on convolutional neural networks (CNN) and machinelearning algorithms (MLAs) for classification. Several individuals are unableto identify rip currents in order to prevent them. In addition, the absenceof evidence to aid in training and validating hazardous systems hinders attempts to predict rip currents. Security cameras and mobile phones have stillimages of something like the shore pervasive and represent a possible causeof rip current measurements and management to handle this hazards accordingly. This work deals with developing detection systems from still beachimages, bathymetric images, and beach parameters using CNN and MLAs.The detection model based on CNN for the input features of beach imagesand bathymetric images has been implemented. MLAs have been applied todetect rip currents based on beach parameters. When compared to other detection models, bathymetric image-based detection models have significantlyhigher accuracy and precision. The VGG16 model of CNN shows maximumaccuracy of 91.13% (Recall = 0.94, F1-score = 0.87) for beach images. Forthe bathymetric images, the highest performance has been found with anaccuracy of 96.89% (Recall= 0.97, F1-score=0.92) for the DenseNet model of CNN. The MLA-based model shows an accuracy of 86.98% (Recall=0.89,F1-score= 0.90) for random forest classifier. Once we know about the potential zone of rip current continuosly generating rip current, then the coastalregion can be managed accordingly to prevent the accidents occured due tothis coastal hazards

ASP.NET-Tutor: Intelligent Tutoring System for leaning ASP.NET

ASP.net is one of the most widely used languages in web developing of its many advantages, so there are many lessons that explain its basics, so it should be an intelligent tutoring system that offers lessons and exercises for this language.why tutoring system? Simply because it is one-one teacher, adapts with all the individual differences of students, begins gradually with students from easier to harder level, save time for teacher and student, the student is not ashamed to make mistakes, and more. Therefore, in this paper, we describe the design of an Intelligent Tutoring System for teaching ASP.net to help students learn ASP.net easily and smoothly. Tutor provides beginner level in ASP.net. Finally, we evaluated our tutor and the results were excellent by students and teacher

Microfluidic array for simultaneous detection of dna oxidation and dna-adduct damage

Exposure to chemical pollutants and pharmaceuticals may cause health issues caused by metabolite-related toxicity. This paper reports a new microfluidic electrochemical sensor array with the ability to simultaneously detect common types of DNA damage including oxidation and nucleobase adduct formation. Sensors in the 8-electrode screen-printed carbon array were coated with thin films of metallopolymers osmium or ruthenium bipyridyl-poly( vinylpyridine) chloride (OsPVP, RuPVP) along with DNA and metabolic enzymes by layer-by-layer electrostatic assembly. After a reaction step in which test chemicals and other necessary reagents flow over the array, OsPVP selectively detects oxidized guanines on the DNA strands, and RuPVP detects DNA adduction by metabolites on nucleobases. We demonstrate array performance for test chemicals including 17 beta-estradiol (E-2), its metabolites 4-hydroxyestradiol (4-OHE2), 2-hydroxyestradiol (2-OHE2), catechol, 2-nitrosotoluene (2-NO-T), 4-(methylnitrosamino)-1-(3-pyridyl)1-butanone (NNK), and 2-acetylaminofluorene (2-AAF). Results revealed DNA-adduct and oxidation damage in a single run to provide a metabolic-genotoxic chemistry screen. The array measures damage directly in unhydrolyzed DNA, and is less expensive, faster, and simpler than conventional methods to detect DNA damage. The detection limit for oxidation is 672 8-oxodG per 10(6) bases. Each sensor requires only 22 ng of DNA, so the mass detection limit is 15 pg (similar to 10 pmol) 8-oxodG