Interpretable Learning Model for Lower Dimensional Feature Space: A Case study with Brown Spot Detection in Rice Leaf
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Detecting brown spot in rice leaf is an urgent complication in the agricultural field as Brown Spot disease lessen the rice yield remarkably. Several segmentation techniques have been applied to identify and extract the infected portion of the rice-leaf and machine learning algorithms such as decision trees, support vector machines are applied to detect this infection. In particular, a combination of Convolution Neural Networks with these algorithms has also tried to resolve this problem. Although this attempt has achieved success in providing accuracy (96.8%), these kinds of approaches raise issues regarding the size and interpretability of feature space and interpretability of the decision model. Indeed, Deep learning networks automatically create a feature space that usually contains a massive number of features (numerous of them are not necessarily appropriate). This vast number of features extends the non-interpretability of the machine learning model. Furthermore, training the model with these many features is computationally expensive. To resolve these issues, we propose a method to extract a few interpretable features from rice-leaf images and construct a low-dimensional feature space; however, interpretation shows that they deserve significant credit for the decent accuracy of our classification model.
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Zero shot learning Kishor Datta Gupta Zero-shot learning allows a model to recognize classes that it was not trained on by utilizing auxiliary information about both seen and unseen classes during training. The model is trained to predict this auxiliary information, like word embeddings or manually designed features, for the seen classes. During testing, the model predicts the auxiliary information for an unseen class and assigns it to the class whose auxiliary information is closest, even if that class was not part of the training data. This allows the model to generalize to new classes without requiring labeled examples of those classes.
Using Negative Detectors for Identifying Adversarial Data Manipulation in Mac...
Using Negative Detectors for Identifying Adversarial Data Manipulation in Mac...Kishor Datta Gupta With the increased popularity of Machine Learning (ML) in real-world applications, adversarial attacks are emerging to subvert the ML-based decision support systems. It appears that the existing adversarial defenses are ineffective against adaptive attacks since these are highly depend on knowledge of prior attacks and the ML model architecture. To alleviate the challenges, We propose a negative filtering strategy that does not require any adversarial knowledge and can work independent of ML models. This filtering strategy relies on salient features of clean (training) data and employs a complementary approach to cover possible attack surface in an application. Our empirical experiments with different data sets demonstrate that the negative filters could effectively detect wide-range of adversarial inputs and update itself to protect against adaptive attacks.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta —Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
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Machine learning in computer securityKishor Datta Gupta Machine learning can be applied in various areas of computer security like network security, endpoint protection, application security, user behavior analysis, and process behavior analysis. Some common machine learning techniques that are useful for security include regression for prediction and detection of anomalies, classification to identify threats and attacks, and clustering for forensic analysis and to detect outliers. Example applications of machine learning in security include using regression to detect anomalies in network traffic, classification to identify malware, and clustering to separate malware from legitimate files.
Policy Based reinforcement Learning for time series Anomaly detection
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understanding the pandemic through mining covid news using natural language p...
understanding the pandemic through mining covid news using natural language p...Kishor Datta Gupta This document summarizes a research presentation on analyzing Covid-19 news reports from newspapers in developed and developing countries using natural language processing. It introduces the research aim to understand how newspapers portray the pandemic using NLP techniques on reports from the US and Bangladesh. The researchers collected over 1000 news articles to create the NNK Dataset, which they preprocessed and analyzed to extract keywords, sentiments, and case numbers. Word clouds of frequent terms and numeric extractions showed how coverage evolved over time. The dataset was made publicly available to encourage further analysis of portraying pandemics through newspapers.
Different representation space for MNIST digit
Different representation space for MNIST digitKishor Datta Gupta The document discusses using different representation spaces for digits when classifying MNIST data. It shows that classifying digits when each digit has its own best representation space that other digits are compared to leads to much higher accuracy, ranging from 97-99%, compared to using the same representation space for all digits which only achieves 64% accuracy.
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...Kishor Datta Gupta Artificial intelligence persists on being a right-hand tool for many branches of biology. From preliminary advices and treatments, such as understanding if symptoms related to fever or cold, to critical detection of cancerous cell or classification of X-rays, traditional machine learning and deep learning techniques achieved remarkable feats. However, total dependency on machine-based prediction is yet a far fetched concept. In this paper, we provide a framework utilizing several Natural Language Processing (NLP) algorithms to construct a comparative analysis. We create an ensemble of top-performing algorithms to accomplish classification task on medical reports. We compare both the traditional machine learning and deep learning techniques and evaluate their probabilities of being reliable on analyzing medical diagnosis. We concluded that an ensemble approach can provide reliable outcomes with accuracy over 92% and that the current state of the art is unequipped to provide the result with the standard needed for health sectors but an ensemble of these techniques can be a pathway for future research direction.
Conference: IEEE 11th Annual Information Technology, Electronics and Mobile Communication Conference (IEEE IEMCON 2020)At: Vancouver
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Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Tech...Kishor Datta Gupta Adversarial attacks are considered security risks for Artificial Intelligence-based systems. Researchers have been studying different defense techniques appropriate for adversarial attacks. Evaluation strategies of these attacks and corresponding defenses are primarily conducted on trivial benchmark analysis. We have observed that most of these analyses have practical limitations for both attacks and for defense methods. In this work, we analyzed the adversarial attacks based on how these are performed in real-world problems and what steps can be taken to mitigate their effects. We also studied practicability issues of well-established defense techniques against adversarial attacks and proposed some guidelines for better and effective solutions. We demonstrated that the adversarial attacks detection rate and destruction rate co-related inversely, which can be used in designing defense techniques. Based on our experimental results, we suggest an adversarial defense model incorporating security policies that are suitable for practical purposes.
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Adversarial Input Detection Using Image Processing Techniques (IPT)
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Clustering report
Clustering reportKishor Datta Gupta This document discusses clustering clean and adversarial images from the MNIST dataset using K-means, LDA, and T-SNE clustering methods. It contains 10,000 clean images and 10,000 adversarial images generated using the FGSM attack method from 10 classes in MNIST. The document applies principal component analysis to extract features from the images before clustering them to visualize how the different methods group the clean and adversarial samples.
Basic digital image concept
Basic digital image conceptKishor Datta Gupta This document discusses basic digital image concepts including image data representations, color channels, bit depth, CMYK vs RGB color models, image blur filters using kernels, and kernel operations used in convolutional neural networks (CNN).
An empirical study on algorithmic bias (aiml compsac2020)
An empirical study on algorithmic bias (aiml compsac2020)Kishor Datta Gupta In all goal-oriented selection activities, an existence of certain level of bias is unavoidable and may be desired for efficient artificial intelligence based decision support systems. However, a fair independent comparison of all eligible entities is essential to alleviate explicit bias in competitive marketplace. For example, searching online for a good or service, it is expected that the underlying algorithm will provide fair results by searching all available entities in the category mentioned. However, a biased search can make a narrow or collaborative query, ignoring competitive outcomes, resulting customers in costing more or getting lower quality products or services for the money they spend. This paper describes algorithmic bias in different contexts with examples and scenarios, best practices to detect bias, and two case studies to identify algorithmic bias.
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...Kishor Datta Gupta This document proposes a hybrid PoW-PoS consensus protocol to prevent 51% attacks in cryptocurrency systems. The key points are:
1) A 51% attack occurs when a single entity gains over 50% of the network's computational power, allowing them to manipulate the blockchain.
2) Existing solutions have limitations like special validators, inconsistent block times, or reliance on another network.
3) The proposed solution uses PoW and PoS sequentially in each node, adjusting difficulty to maintain consistent block times. It penalizes malicious nodes and distributes profits proportionally.
4) Analysis shows the hybrid protocol maintains decentralization and the blockchain properties better than alternatives like Casper, Decred or Komodo
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Interpretable Learning Model for Lower Dimensional Feature Space: A Case study with Brown Spot Detection in Rice Leaf
- 1. Interpretable Learning Model for Lower Dimensional Feature Space: A Case study with Brown Spot Detection in Rice Leaf Presented by: Md. Amit Khan Authors Md. Amit Khan1 Nafiz Sadman1 Kishor Datta Gupta2 Jesan Ahammed Ovi3 1. Silicon Orchard Ltd, Bangladesh 2. University of Memphis, TN, USA 3. East West University, Bangladesh
- 2. Brown Spot Disease ● Caused By Bipolaris Oryzae and Cochliobolus Miyabeanus ● Major Symptom is cylindrical, circular and oval shaped brown dots on leaves ● Is cureable if detected early
- 3. Data Set ● Downloaded from Kaggle ● Four classes (Brown Spot, Healthy, Hispa, Leaf Blast) ● Each Class has 523 images
- 4. Previous Attempts ● Decision Tree with 10-fold cross validation ● SVM classifier to detect three kinds of rice diseases ● Combined approach of image processing and soft computing ● Rule Mining Approach ● Combination of a CNN model with a SVM classifier
- 5. Proposed Method 1 2 3
- 6. 1. Extract Aggregated Features From Images ● Aggregated Laplacian Coefficient ● Estimated Brown Spot Area ● Sum of contours area
- 7. ● Laplacian Based features to identify the brown Spot’s color and shape properties ● Measured for red, green and blue channels Individually ● Aggregated laplacian coefficient by summing each laplacian
- 8. Estimated Brown Spot Area ● Images are converted into binary images ● All contours are obtained ● Areas of the contours are calculated ● Second largest area of the contours are considered to be the brown spot area
- 9. 2. Train The Classifier ● A random forest classifier was chosen ● Best set of parameters were chosen by grid search ● Data set was divided into two sets (training and testing)
- 10. Experimental Results and Analysis Model TP FP TN FN Logistic Regression 89 25 9 87 Decision Tree 87 27 8 88 Random Forest 91 23 5 91
- 11. Experimental Results and Analysis Comparative Performance Model Precision Recall F-1 Score Logistic Regression 0.84 0.84 0.84 Decision Tree 0.84 0.84 0.83 Random Forest 0.87 0.87 0.87
- 12. 3. Interpretation of Classifier Decision Two types of explainer were used to provide interpretation of the model: 1. Overall Explainer 2. Local Explainer
- 13. Overall Explainer Feature importance Variable Importance Laplacian Blue 0.380751 Laplacian Green 0.258484 Estimated Brown Spot Area 0.190786 Sum of contours Area 0.094294 Laplacian Red 0.075685
- 14. Local Explainer 3 Variables with the most contributions for increasing the probability of brown spot Variable Impact Laplacian Blue +32% Estimated Brown Spot Area +16% Sum of Contours Area +6%
- 15. Local Explainer 3 Variables with the most contributions for decreasing the probability of brown spot Variable Impact Estimated Brown Spot Area -14% Laplacian Green -13% Laplacian Red -5%
- 16. Future Work ● Extended method to detect Hispa and Leaf blast ● More Specific feature set ● Apply to other crops
- 17. Q/A