Browsing by Author "Syed, Aamer"

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    Early detection of chronic kidney failure
    (AIKTC, 2019-05) Syed, Aamer
    The growing global burden of Non-communicable disease(NCD’s) worldwide, is increasing day by day. The chronic renal failure is a type of NCD. These chronic disease are one of the leading cause of death. It becomes important for our society, to detect and cure it as early as possible. Our system aims to predict the possibility of chronic renal failure, i.e the chances of kidney failure of a patient. Huge amount of patient’s data and their case histories are stored from years and years, and yet not being used. This data can be used to predict, using massive data sets, by categorizing valid and unique patterns in data. We aim to make a system through which people can regularly check the risk to have chronic renal failure. Keywords: CRF(Chronic Renal Failure), NCD(Non Communicable Disease), Massive Data sets, Prediction Algorithms, Machine Learning.
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    Location based places of interest
    (AIKTC, 2020-05) Syed, Aamer; Ashfaque, Ahmad (15CO14); Khan, Mohammed Yusuf (16CO31); Khan, Mohammed Hasmuddin (16CO30)
    Location based Services offer many advantages to the mobile users to retrieve the information about their current location and process that data to get more useful information near to their location.With the help of this app in phones and through Web Services using GPRS, Location based Services can be implemented on Android based smart phones to provide these value-added services: providing routing information, helping them find nearby hotels,Malls,Park,Hospitals . We propose the implementation of Location based services through Google Web Services and Google map APIs on Android Phones to give multiple services to the user based on their Location.
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    MoMoS- Mobile application monitoring software
    (AIKTC, 2017-05) Ansari, Mukhtar; Syed, Aamer; Nazir, Umar (12CO51); Barmare, Eilaf (12CO25); Mulla, Taariq (12CO50)
    Android is a widely used mobile platform and since it’s evolution there has been rise in development of Android application’s to fulfill the requirement of user’s. As a developer’s point of view, one should know how his application is performing on PlayStore. Analysis of Android application gives a brief idea of capabilities of application’s and shows it’s real time behaviour on PlayStore. For the very purpose Analytic tools are used to monitor the app behaviour. This project proposes an architectural model for monitoring of an Android app, under which various parameter’s are considered. The proposal of this project includes real-time monitoring of Android application to facilitate developers with certain useful parameter’s which will help them to track the current and future updates of the application.
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    Parley; hand gesture recognition for deaf and dumb people
    (AIKTC, 2021-05) Syed, Aamer; Mansoori, Abdul Rehman Abdullah (17CO34); Siddique, Zeeshan Azam (17CO50); Pathan, Yunus Abdul Bari (16CO43); Khan, Irfan Iqbal (15CO18)
    1. According to 2011 census of India states that 7 million Indians are suffering from hearing and speech impairment. 2. Sign languages are a visual representation of thoughts through hand gestures, facial expressions, and body movements. 3. Their circle is very much limited. They should not be part of the deaf world alone,which seems cloistered sometimes. 4. Recognition of sign language can be done in two ways, either glove based recognition or vision based Recognition. So, the proposed system uses the vision based recognition method. 5. So conversing in their regional sign language brings more comfort for the people to share their ideas and thoughts among their near and dears. Keywords: Indian Sign language (ISL), Sign language recognition, Machine Learning, Algorithm, NLP, API, Dataset, Framework, User Interface, Hand Gesture, Convolutional Neural Network (CNN).
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    Placement prediction system
    (AIKTC, 2021-05) Syed, Aamer; Patni, Aamir Sattar (18DCO13); Bandar, Zishan Yusuf (18DCO03); Shaikh, Nousheen Mohammed Sadique (18DCO18); Shaikh, Zara Misbah Anjum (18DCO20)
    Engineering students are skeptical about what they want to pursue after graduation. With wide options available, ranging from campus recruitment to Masters, students are perplexed, adding factors like salaries and different job opportunities makes it even worse. There aren’t any reliable platforms where a student can predict the outcomes from the start of engineering and take actions to bridge this gap for a better future. Students studying in Engineering colleges feel the exigency to know where they stand in comparison to others, and what kind of placement they would get. The training and placement offices come in the picture when a student enters final year, but they are of no use to a student planning for future studies.Placement of students is one of the most important objectives of an educational institution. Institutions make great efforts to achieve placements for their students.The objective is to predict the students getting placed for the current year by analyzing the data collected from previous years students.Prediction about the student’s performance is an integral part of an education system, as the overall growth of the student is directly proportional to the success rate of the students in their examinations and extracurricular activities. Therefore, there are many situations where the performance of the student needs to be predicted, for example, in identifying weak performing students and taking actions for their betterment. The students have no platform to check their current position and build on their strengths. The platforms currently available, have not been trained on real and complete data sets, and do not learn from their wrong predictions which reduces the accuracy, in the long term. We aim to develop one. To ensure effective results, the model will be trained on a real data set and a vast number of qualitative as well as quantitative parameters will be considered.This model is proposed with an algorithm to predict the same. The data has been collected by the institution for which prediction is going to be done and by applying suitable data pre-processing techniques or to analyze previous year’s student’s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions.