Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

Although the Titanic disaster happened just over one hundred years ago, it still appeals researchers to understand why some passengers survived while others did not. With the use of a machine learning tool (JustNN) and the provided dataset we study which factors or classifications of passengers have a strong relationship with survival for passengers that took that trip on 15th of April, 1912. The analysis seeks to identify characteristics of passengers - cabin class, age, and point of departure – and (...) that relationship to the chance of survival for the disaster. Furthermore, we developed a model for classifying passengers. The model was trained and tested and we found the accuracy to be more than 99.28%. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

Plant production provides human and animal life with different requirements. The concern of workers in agriculture in general and those interested in plant diseases, in particular, has been focused on protection from all that is expected to have problems of production. As environmental conditions play a critical role in the treatment of diseases, the plant is prepared and rendered more susceptible to production, which is exposed and may result in the loss of the entire crop. Objectives: The main goal of (...) this expert system is to get the appropriate diagnosis of Apple disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Farmers, people interested in agriculture and agricultural engineers in diagnosing many of the apple diseases such as : Apple scab, Marssonina leaf blotch (pre mature leaf fall) , Black rot canker, Collar rot , Powdery mildew ,Sooty blotch and fly speck, Apple mosaic and other virus diseases , Alternaria leaf spot/blight , Core rot , Brown rot , White rot / root rot , Seedling blight . The proposed expert system presents an overview about apple diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. CLIPS with Delphi were used for designing and implementing the proposed expert system. Results: The proposed apple diseases diagnosis expert system was evaluated by Farmer, Agricultural experts and teachers of the Gaza School of Agriculture and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Farmers, and those interested in agriculture with apple disease and recent graduate students. (shrink)

The aim of this research is to study the performance of JustNN environment that have not been previously examined to care of this blood donation problem forecasting. An Artificial Neural Network model was built to understand if performance is considerably enhanced via JustNN tool or not. The inspiration for this study is that blood request is steadily growing day by day due to the need of transfusions of blood because of surgeries, accidents, diseases etc. Accurate forecast of the number of (...) blood donors can help medical professionals know the future supply of blood and plan consequently to attract volunteer of blood donors to fulfill the demand. We found that the ANN model using JustNN tool led to the best test set performance accuracy of (99.31%), which is better than other studies. (shrink)

Liver diagnosis at an early stage is essential for enhanced handling. Precise classification is required for automatic recognition of disease from data samples (utilizing data mining for classification of liver patients from healthy ones). In this study, an artificial neural network model was designed and developed using JustNN Tool for predicting weather a person is a liver patient or not based on a dataset for liver patients. The main factors for input variables are: Age, Gender, Total Bilirubin, Direct Bilirubin, Alkphos (...) Alkaline Phosphotase. Sgpt Alamine Aminotransferase, Sgot Aspartate Aminotransferase, Total Protiens, Albumin, Albumin and Globulin Ratio, and the output variable: Status. The dataset used for training are the data published in the literature for various 583 liver patients. The model was trained and validated, most important factors affecting Status of liver patient identified, and the accuracy for the validation was 99.00%. (shrink)

Background: Plant production provides human and animal life with different requirements. The concern of workers in agriculture in general and those interested in plant diseases, in particular, has been focused on protection from all that is expected to have problems of production. As environmental conditions play a critical role in the treatment of diseases, the plant is prepared and rendered more susceptible to production, which is exposed and may result in the loss of the entire crop. Objectives: The main goal (...) of this expert system is to get the appropriate diagnosis of Black Pepper disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Farmers, people interested in agriculture and agricultural engineers in diagnosing many of the black pepper diseases such as : Foot rot /quick, wilt disease, Pollu disease /anthracnose, Slow decline /slow wilt, Stunt disease, Phyllody disease, Basal wilt, Leaf rot and blight. The proposed expert system presents an overview about black pepper diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. CLIPS with Delphi were used for designing and implementing the proposed expert system. Results: The proposed black pepper diseases diagnosis expert system was evaluated by Farmers, Agricultural experts and Agriculture teachers and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for farmers, and those interested in agriculture with black pepper disease and recent graduate students. (shrink)