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Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

Journal of Information Security 2018年第3期9卷 209-224页

作者： md. hasan hasnain nashif md. Badrul Alam Miah Ahsan Habib Autish Chandra Moulik md. Shariful Islam Mohammad Zakareya Arafat Ullah md. Atiqur Rahman md. Al hasan Department of Information and Communication Technology (ICT) Mawlana Bhashani Science and Technology University (MBSTU) Tangail Bangladesh

Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.

关键词： Signature Handwritten Character Image Processing Feature Extraction Neural Network Recognition

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Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

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World Journal of Engineering and Technology 2018年第4期6卷 854-873页

作者： Shadman nashif md. Rakib Raihan md. Rasedul Islam Mohammad hasan Imam Department of Computer Science & Engineering Rajshahi University of Engineering & Technology (RUET) Rajshahi Bangladesh Department of Electrical & Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangla-desh

Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.

关键词： Data Mining Machine Learning IoT (Internet of Things) Patient Monitoring System Heart Disease Detection and Prediction

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