Browsing by Author "Neehal, Nafis"

Now showing 1 - 9 of 9

AcneNet - A deep CNN Based Classification Approach for Acne Classes
(Scopus, 2019) Junayed, Masum Shah; Jeny, Afsana Ahsan; Neehal, Nafis; Atik, Syeda Tanjila
Skin diseases are very common and nowadays easy to get remedy from. But, sometimes properly diagnosing these diseases can be quite troublesome due to the stiff hard-to-discriminate nature of the symptoms they exhibit. Deep Neural Networks, since its recent advent, has started outperforming different algorithms in almost every sectors. One of the problem domains, where Deep Neural Networks are really thriving today, is Image Classification and Object and Pattern Discovery from images. A special type of Deep Neural Network is Convolutional Neural Networks (CNN), which are being extensively used for different sorts of computer vision and image classification related problems. Hence, we have proposed a novel approach, where we have developed and used a Deep Residual Neural Network model for classifying five classes of Acnes from images. Our model has achieved an approximate accuracy as much as 99.44% for one class, and the rest were also above 94% with fairly high precision and recall score.
An Empirical Study of Cervical Cancer Diagnosis using Ensemble Methods
(Scopus, 2019-05-05) Karim, Enamul; Neehal, Nafis
Cervical Cancer, being one of the most pressing issues now-a-days, needs to be addressed properly. With a view to achieving an accurate diagnosis method for Cervical Cancer by screening the risk factors, different machine learning approaches have been taken over time. But by analyzing the performances of most of state-of-the-art approaches, it was inferred that there is still room for improvement by developing a more accurate model. Hence, in this paper an approach using ensemble methods with SVM as the base classifier has been taken. The ensemble method with Bagging technique achieved an accuracy of 98.12% with very high precision, recall and f-measure value.
Cloud-POA: A cloud-based map only implementation of PO-MSA on Amazon multi-node EC2 Hadoop Cluster
(IEEE, 2018-02-08) Neehal, Nafis; Karim, Dewan Ziaul; Islam, Ashraful
Sequence alignment in bioinformatics and computational biology has always been a challenging task. With Next Generation Sequencing (NGS) techniques in hand, researchers are now capable of studying biological systems at a level never been possible before. Scientists now have billions of bytes of biological data to work with, trillions of sequences to align. But this comes at a cost of requiring computing machines having a tremendous amount of computational and analytical power. Purchasing this huge amount of hardware and setting up a standalone infrastructure would not only cost an unnecessarily massive amount of money and labor but also would become troublesome to maintain. Moreover, for aligning a huge number of DNA or Protein sequences a scalable multiple sequence alignment (MSA) algorithms is needed with decent accuracy. In such context, this paper presents a novel implementation of Partial Order Alignment (POA) algorithm on a multi-node Hadoop Cluster running on MapReduce framework. The implementation was done in Amazon AWS platform with multiple EC2 instances. It is a map-only implementation with Hadoop Streaming. The result of this implementation shows a drastic reduction in runtime with no accuracy degradation.
Exnet
(Communications in Computer and Information Science, Springer, 2019-07-20) Haque, Sadeka; Rabby, AKM Shahariar Azad; Laboni, Monira Akter; Neehal, Nafis; Hossain, Syed Akhter
Pose detection estimate human activity in images or video frames using computer vision technique. Pose detection has many applications, such as body to augmented reality, fitness, animation etc. ExNET represents a way to detect human pose from 2D human exercises image using Convolutional Neural Network. In recent time Deep Learning based systems are making it possible to detect human exercise poses from images. We refer to the model we have built for this task as ExNET: Deep Neural Network for Exercise Pose Detection. We have evaluated our proposed model on our own dataset that contains a total of 2000 images. And those images are distributed into 5 classes as well as images are divided into training and test dataset, and obtained improved performance. We have conducted various experiments with our model on the test dataset, and finally got the best accuracy of 82.68%.
Friend Recommendation System in Social Network using Personality Analysis and User Behavior
(IUT, CSE, 2016-11-20) Neehal, Nafis; Noor, Shoaib Bin
Social networking is a tool used by people all around the world. Its purpose is to promote and aid communication. Social networks, such as Facebook, were created for the sole purpose of helping individuals communicate. These networks are becoming the modern way to make friends. These new friends communicate through these networks. There exist recommendation systems in all the social networks which help users to nd new friends and connect to more peoples. With friends, there comes a strong friend recommendation system also. The existing social networks do have their own friend recommendation system which is based on the friends of friends' methodology. This graph based friend recommendation system is not very accurate most of the time and drive users to wrong direction. We tried to make this recommendation system more accurate adding some extra layers of personality analysis and user behavior. With the vast amount of user data, our system will gure out each user's personality traits and behavior which will be used to help him/her nding out new users with same nature.
Incept-N
(Communications in Computer and Information Science, Springer, 2019-07-16) Junayed, Masum Shah; Jeny, Afsana Ahsan; Neehal, Nafis; Ahmed, Eshtiak; Hossain, Syed Akhter
Nationality of a human being is a well-known identifying characteristic used for every major authentication purpose in every country. Albeit advances in application of Artificial Intelligence and Computer Vision in different aspects, its’ contribution to this specific security procedure is yet to be cultivated. With a goal to successfully applying computer vision techniques to predict a human’s nationality based on his facial features, we have proposed this novel method and have achieved an average of 93.6% accuracy with very low mis-classification rate.
InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games
(Elsevier B.V., 2018-11-19) Islam, Mohammad Shakirul; Foysal, Ferdouse Ahmed; Neehal, Nafis; Karim, Enamul; Hossain, Syed Akhter
Sports activities are an integral part of our day to day life. Introducing autonomous decision making and predictive models to recognize and analyze different sports events and activities has become an emerging trend in computer vision arena. Albeit the advances and vivid applications of artificial intelligence and computer vision in recognizing different popular western games, there remains a very minimal amount of efforts in the application of computer vision in recognizing traditional Bangladeshi games. We, in this paper, have described a novel Deep Learning based approach for recognizing traditional Bengali games. We have retrained the final layer of the renowned Inception V3 architecture developed by Google for our classification approach. Our approach shows promising results with an average accuracy of 80% approximately in correctly recognizing among 5 traditional Bangladeshi sports events.
Runtime Optimization of Identification Event in ECG Based Biometric Authentication
(2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019, Cornell University, 2018-05-15) Neehal, Nafis; Karim, Dewan Ziaul; Banik, Sejuti; Anika, Tasfia
Biometric Authentication has become a very popular method for different state-of-the-art security architectures. Albeit the ubiquitous acceptance and constant development of trivial biometric authentication methods such as fingerprint, palm-print, retinal scan etc., the possibility of producing a highly competitive performance from somewhat less-popular methods still remains. Electrocardiogram (ECG) based biometric authentication is such a method, which, despite its limited appearance in earlier research works, are currently being observed as equivalently high-performing as other trivial popular methods. In this paper, we have proposed a model to optimize the runtime of identification event in ECG based biometric authentication and we have achieved a maximum of 79.26% time reduction with 100% accuracy.
Shot-Net
(Communications in Computer and Information Science, Springer, 2019-07-20) Foysal, Md. Ferdouse Ahmed; Islam, Mohammad Shakirul; Karim, Asif; Neehal, Nafis
Artificial Intelligence has become the new powerhouse of data analytics in this technological era. With advent of different Machine Learning and Computer Vision algorithms, applying them in data analytics has become a common trend. However, applying Deep Neural Networks in different sport data analyzing tasks and study the performance of these models is yet to be explored. Hence, in this paper, we have proposed a 13 layered Convolutional Neural Network referred as “Shot-Net” in order to classifying six categories of cricket shots, namely Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot and Leg Glance Shot. Our proposed model has achieved fairly high accuracy with low cross-entropy rate.