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Mental Health and Sensing [article]

Abdul Kawsar Tushar, Muhammad Ashad Kabir, Syed Ishtiaque Ahmed
2020 arXiv   pre-print
Mental health is a global epidemic, affecting close to half a billion people worldwide. Chronic shortage of resources hamper detection and recovery of affected people. Effective sensing technologies can help fight the epidemic through early detection, prediction, and resulting proper treatment. Existing and novel technologies for sensing mental health state could address the aforementioned concerns by activating granular tracking of physiological, behavioral, and social signals pertaining to
more » ... blems in mental health. Our paper focuses on the available methods of sensing mental health problems through direct and indirect measures. We see how active and passive sensing by technologies as well as reporting from relevant sources can contribute toward these detection methods. We also see available methods of therapeutic treatment available through digital means. We highlight a few key intervention technologies that are being developed by researchers to fight against mental illness issues.
arXiv:2009.12488v1 fatcat:afwy7oknnvfm7lq7m5q7j4aioq

Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks [article]

Akm Ashiquzzaman, Abdul Kawsar Tushar
2017 arXiv   pre-print
Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. DBLP:journals/corr/abs-1003-1891 has pioneered the research for handwritten digit recognition in Arabic. In this
more » ... er, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods. The proposed model gives 97.4 percent accuracy, which is the recorded highest accuracy of the dataset used in the experiment. We also propose a modification of the method described in DBLP:journals/corr/abs-1003-1891, where our method scores identical accuracy as that of DBLP:journals/corr/abs-1003-1891, with the value of 93.8 percent.
arXiv:1702.04663v1 fatcat:if7dvel6c5hqfdzox63ypwubda

Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network [article]

Akm Ashiquzzaman, Abdul Kawsar Tushar, Md. Rashedul Islam, Jong-Myon Kim
2017 arXiv   pre-print
Augmented accuracy in prediction of diabetes will open up new frontiers in health prognostics. Data overfitting is a performance-degrading issue in diabetes prognosis. In this study, a prediction system for the disease of diabetes is pre-sented where the issue of overfitting is minimized by using the dropout method. Deep learning neural network is used where both fully connected layers are fol-lowed by dropout layers. The output performance of the proposed neural network is shown to have
more » ... ormed other state-of-art methods and it is recorded as by far the best performance for the Pima Indians Diabetes Data Set.
arXiv:1707.08386v1 fatcat:mj72vv53ffelnje3odoqh34pdu

Chord Angle Deviation using Tangent (CADT), an Efficient and Robust Contour-based Corner Detector [article]

Mohammad Asiful Hossain, Abdul Kawsar Tushar
2017 arXiv   pre-print
Detection of corner is the most essential process in a large number of computer vision and image processing applications. We have mentioned a number of popular contour-based corner detectors in our paper. Among all these detectors chord to triangular arm angle (CTAA) has been demonstrated as the most dominant corner detector in terms of average repeatability. We introduce a new effective method to calculate the value of curvature in this paper. By demonstrating experimental results, our
more » ... technique outperforms CTAA and other detectors mentioned in this paper. The results exhibit that our proposed method is simple yet efficient at finding out corners more accurately and reliably.
arXiv:1702.04843v1 fatcat:w2equ6m5pzelzpa3amlg4kb7nm

A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks [article]

Abdul Kawsar Tushar, Akm Ashiquzzaman, Afia Afrin, Md. Rashedul Islam
2017 arXiv   pre-print
Abdul Kawsar Tushar and Akm Ashiquzzaman contributed equally to this work.  ... 
arXiv:1707.08385v1 fatcat:uvj5sex5hzcyhdwxra622cek4a

Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks [article]

Akm Ashiquzzaman, Abdul Kawsar Tushar, Md Ashiqur Rahman
2022 arXiv   pre-print
Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic. Due to its enormous application many works have been done in this field focusing on different languages. Arabic, being a diversified language has a huge scope of research with potential challenges. A convolutional neural network model for recognizing handwritten numerals in Arabic language
more » ... is proposed in this paper, where the dataset is subject to various augmentation in order to add robustness needed for deep learning approach. The proposed method is empowered by the presence of dropout regularization to do away with the problem of data overfitting. Moreover, suitable change is introduced in activation function to overcome the problem of vanishing gradient. With these modifications, the proposed system achieves an accuracy of 99.4\% which performs better than every previous work on the dataset.
arXiv:1708.05969v5 fatcat:waarcqza4bbwffeq6ibkrdpgg4

ICECE 2020 at a Glance

2020 2020 11th International Conference on Electrical and Computer Engineering (ICECE)  
Md Kawsar Alam, BUET and Dr. Ahmed Zubair, BUET Session Chair(s): Prof. Mohammad Jahangir Alam, BUET and Dr.  ...  Mijanur*; Abdul Hasib Chowdhury, Prof. Dr. Malek, Ihtesham Ibn*; Islam, Md. Zahidul; Hasan, Mahmud; SUHANUR RAHMAN, MOHAMMAD; Abdul Hasib Chowdhury, Prof.  ... 
doi:10.1109/icece51571.2020.9393149 fatcat:cqtcymeo4zc7dexqubpye3kbtu

New Media in Film Distribution in Bangladesh: Bane or Boon?

Saiyeed Shahjada Al Kareem
2021 Athens Journal of Mass Media and Communications  
Aziz Chairman Feature Film Tushar Kathachitra Abdul Mabud Kawsar Distributor and Owner Feature Film Malancha Chalacchitro Miah Alauddin Distributor and Owner Feature Film Modhumita Movies  ...  Respondent Designation Feature Film Ashirbad Chalacchitro Razib Ahsan Executive Director Feature Film Janani Kothachitra Sajjad Hossain Distributor and Owner Feature Film Jazz Multimedia Abdul  ... 
doi:10.30958/ajmmc.7-4-3 fatcat:csyb7zicwbga3higt4vzvofdmm