Papers by Faisal Muhammad Shah
1st International Conference on Advances in Science, Engineering and Robotics Technology 2019 (ICASERT 2019), 2019
When it comes down to buying products from online shops, one of the key factor that influences a ... more When it comes down to buying products from online shops, one of the key factor that influences a buyer are the reviews associated with a product. While buying people try to understand the quality and authenticity of the product by reading the previous user feedback. And sellers have started taking advantage of it. Putting fake and spam reviews to deceive the buyers is a common strategy mostly used by newcomers. But these reviews are important when it comes to deciding whether to buy a product or not. We propose a method to detect these fake reviews from Amazon Review Dataset. Rather than using traditional machine learning classifiers we have used boosting algorithms to improve the accuracy of the traditional approach. In this approach, a significant increase in accuracy has been achieved by boosting weak learners. Up to 93% accuracy has been achieved when tried to detect fake reviews where traditional machine learning algorithms achieve an accuracy of up to 89%.
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Medical Image is one of the most imperative field in Image Processing. Working on this field is a... more Medical Image is one of the most imperative field in Image Processing. Working on this field is an ambitious task as well as challenging and tumor segmentation from a medical image is the tenacious task. Over the decades researchers went through considerable development to segment the tumor. Researchers developed various methods to articulate the carcinoma. Numerous segmentation techniques such as threshold based, region based, clustering based segmentation etc. have been applied for this purpose. Perceiving the current prominence in this terrain, we glean all the analytical information in addition to a brief analysis. In this paper, we entailed various image segmentation techniques, different types of existing algorithms based on some aspects of brain MRI images and at last we ended with a brief discussion of a few challenges for our future work.
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5th International Conference on Advances in Electrical Engineering (ICAEE-2019), 2019
People show emotions for everyday communication. Emotions are identified by facial expressions, be... more People show emotions for everyday communication. Emotions are identified by facial expressions, behavior, writing, speaking,gestures and physical actions. Emotion plays a vital role in the interaction between two people. The detection of emotions through text is a challenge for researchers. Emotion detection from the text can be useful for real-world application. Automatic emotion detection in the original text aims to recognize emotions in any digital medium by using natural language processing techniques and different approaches. Enabling machines with the ability to recognize emotions in a particular kind of text such as twitter’s tweet has important applications in sentiment analysis and affective computing. We have worked on the newly published gold dataset(AIT-2018)and propose a model consisting of lexical based using WordNet-Affect and EmoSenticNet with supervised classifiers for detecting emotions in a tweet text.
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International Journal of Computer Applications 178(48):42-48, 2019
E-commerce proved its importance based on the fact where time is the essence. People are relying ... more E-commerce proved its importance based on the fact where time is the essence. People are relying on e-commerce more than before. With e-commerce comes a huge amount of user feedback based on the products they buy. As the internet has become cheaper and easy to get, more people are getting connected through different social media and platform where they are expressing product-related feedbacks. With the rise of e-commerce, people are relying more on product reviews to get a clear view and user experience. But there is no convincing way to authenticate the reviews posted on products on e-commerce websites. To generate more revenue and fulfill some immoral benefits, some sellers are making investments and hiring people to post fake reviews. These fake reviews are generated to convince people to buy the product. To detect these fake reviews, several methodologies were introduced. Most of the models are supervised models which rely on pseudo fake reviews or large scale labeled dataset. In this paper, a model has been proposed with a new technique which combines two different types of learning methods (active and supervised) by creating a manually labeled dataset. This model has 4 different filtering phases that are based on TF-IDF, Countvectorizer and n-gram features of the review content and then Principal Component Analysis to reduce the feature set. It achieves a very encouraging result while working on 2000 reviews from Amazon. In the best case precision, recall, and f-score are slightly above 91% and the accuracy achieved is up to 90%. After comparing the results with similar successful methods where PCA is used as a feature selection technique, it is quite clear that the proposed model is efficient and encouraging.
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2018 IEEE International Conference on Innovative Research and Development (ICIRD), 2018
The world we see nowadays is becoming more digitalized. In this digitalized world e-commerce is t... more The world we see nowadays is becoming more digitalized. In this digitalized world e-commerce is taking the ascendancy by making products available within the reach of customers where the customer doesn't have to go out of their house. As now a day's people are relying on online products so the importance of a review is going higher. For selecting a product, a customer needs to go through thousands of reviews to understand a product. But in this prospering day of machine learning, going through thousands of reviews would be much easier if a model is used to polarize those reviews and learn from it. We used supervised learning method on a large scale amazon dataset to polarize it and get satisfactory accuracy.
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The world we see nowadays is becoming more
digitalized. In this digitalized world e-commerce is t... more The world we see nowadays is becoming more
digitalized. In this digitalized world e-commerce is taking the
ascendancy by making products available within the reach of
customers where the customer doesn’t have to go out of their
house. As now a day’s people are relying on online products so the
importance of a review is going higher. For selecting a product, a
customer needs to go through thousands of reviews to understand
a product. But in this prospering day of machine learning, going
through thousands of reviews would be much easier if a model is
used to polarize those reviews and learn from it. We used
supervised learning method on a large scale amazon dataset to
polarize it and get satisfactory accuracy.
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SAI Intelligent Systems Conference (IntelliSys), 2015
Rapid growth and popularity of internet has re-emphasized the significance of the intrusion detec... more Rapid growth and popularity of internet has re-emphasized the significance of the intrusion detection system in network security. To overcome the vulnerabilities of network security researchers have come up with different frameworks of intrusion detection system using data mining. Feature selection is a significant method to develop a time and cost effective intrusion detection system. The time consumption in building up the classifiers model enhances the efficiency of the system. This work conducted on the analysis of some approaches of intrusion detection using some machine learning methods with wrapper approaches, which is a type of feature selection methodology. Our paper mainly focuses on the classification preciseness of 3 different classifiers using the minimal amount of features selected by three different wrapper search methods on the well-known public type NSL-KDD dataset and showing the comparisons among them. The 3 basic classifiers are Bayesian Network, Naive Bayes and J48. Best First, Genetic Search and Rank Search have been used as the wrapper search methods. The study proposed an ensemble type of a classification model with a hybrid feature selection method based on the research framework. By using the hybrid feature selection method 12 critical features are chosen and with the combination of basic classifiers, a reliable model is developed to differentiate normal and anomaly. Moreover, the result shows a convenient false positive rate of 0.021. Experiment showed that our proposed ensemble approach showed better result than Naive Bayes, Bayesian Network and J48 classifier. Experiments have been conducted on the NSL-KDD dataset using WEKA 3.6 library functions.
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Intrusion Detection System (IDS) is an example of Misuse Detection System that works for detectin... more Intrusion Detection System (IDS) is an example of Misuse Detection System that works for detecting malicious attacks. This can be defined as software for security management. Many researchers have proposed the Intrusion Detection System with different techniques to achieve the best accuracy. In this paper it is projected that intrusion detection system with the amalgamation of k-means clustering and artificial neural network to improve the system. To obtain a better result benchmark dataset was split into training and testing part and then cluster the dataset into five different divisions. After getting the cluster data it has been trained by the different Artificial Neural Networks functions as- Feed Forward Neural Network (FFNN), Elman Neural Network (ENN), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial Basis Neural Network (RBNN). After implementing these functions we have proposed a comparative analysis between them and choose the best accuracy rate among them. Here, it has been proved that, using the clustering technique a better accuracy rate can be found that improve the system with the best neural network functions which is the probabilistic neural network. It is also important to select efficient feature sets for better accuracy.
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Conference Presentations by Faisal Muhammad Shah
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Intrusion Detection System (IDS) predominantly works for detecting malicious attacks. Many resear... more Intrusion Detection System (IDS) predominantly works for detecting malicious attacks. Many researchers have proposed the IDS with different techniques to achieve the best accuracy with the consolidation of Clustering and Artificial Neural Network (ANN). Clustering and ANN based models give better precision rate with better accuracy where attack records are low. Nevertheless, all the features of dataset are not relevant for classifying different attacks. So, feature selection can improve the stability and accuracy of IDS. In this paper, it is proposed that IDS with the amalgamation of best efficient features selected by Principal Component Analysis (PCA) can reduce the computational complexity of the system. It has been combined with the K-means clustering technique to cluster the specific groups of attacks and Artificial Neural Network to get a preeminent output by training the formulation of different base models. The model name has been defined by FP-ANK model. Investigational results have been reported on the NSL-KDD dataset where the accuracy rate associating with other models is distinct to validate the proposed system.
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Papers by Faisal Muhammad Shah
digitalized. In this digitalized world e-commerce is taking the
ascendancy by making products available within the reach of
customers where the customer doesn’t have to go out of their
house. As now a day’s people are relying on online products so the
importance of a review is going higher. For selecting a product, a
customer needs to go through thousands of reviews to understand
a product. But in this prospering day of machine learning, going
through thousands of reviews would be much easier if a model is
used to polarize those reviews and learn from it. We used
supervised learning method on a large scale amazon dataset to
polarize it and get satisfactory accuracy.
Conference Presentations by Faisal Muhammad Shah
digitalized. In this digitalized world e-commerce is taking the
ascendancy by making products available within the reach of
customers where the customer doesn’t have to go out of their
house. As now a day’s people are relying on online products so the
importance of a review is going higher. For selecting a product, a
customer needs to go through thousands of reviews to understand
a product. But in this prospering day of machine learning, going
through thousands of reviews would be much easier if a model is
used to polarize those reviews and learn from it. We used
supervised learning method on a large scale amazon dataset to
polarize it and get satisfactory accuracy.