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adversarial vs nonadversarial clustering
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GAN intro
GAN introduction.pptx
GAN introduction.pptx
Kishor Datta Gupta
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.
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Kishor Datta Gupta
Conference: 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people feel safe using a mainstream word, "encryption". Though encryption protects personal data from an external breach, the companies can still exploit personal data collected from users as they own the encryption keys. We present a naive yet secure approach for recommending movies to consumers without collecting any personally identifiable information. Our proposed approach can assist a movie recommendation system understand user preferences using the user's movie watch-time and watch history only. We conducted a comprehensive and comparative study on the performance of three deep reinforcement learning architectures, namely DQN, DDQN, and D3QN, on the same task. We observed that D3QN outperformed the other two architectures and achieved a precision of 0.880, recall of 0.805, and F1 score of 0.830. The results show that we can build a competitive movie recommendation system using unidentifiable data.
A safer approach to build recommendation systems on unidentifiable data
A safer approach to build recommendation systems on unidentifiable data
Kishor Datta Gupta
Adversarial Attacks and Defense
Adversarial Attacks and Defense
Adversarial Attacks and Defense
Kishor Datta Gupta
Presented in ICML2021 DMMLSys workshop
Who is responsible for adversarial defense
Who is responsible for adversarial defense
Kishor Datta Gupta
Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial Attacks
Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial A...
Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial A...
Kishor Datta Gupta
Zero Shot Learning using word2vec and attribute based image classification
Zero shot learning
Zero shot learning
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.
Using Negative Detectors for Identifying Adversarial Data Manipulation in Mac...
Using Negative Detectors for Identifying Adversarial Data Manipulation in Mac...
Kishor Datta Gupta
Recommended
GAN intro
GAN introduction.pptx
GAN introduction.pptx
Kishor Datta Gupta
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.
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Kishor Datta Gupta
Conference: 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people feel safe using a mainstream word, "encryption". Though encryption protects personal data from an external breach, the companies can still exploit personal data collected from users as they own the encryption keys. We present a naive yet secure approach for recommending movies to consumers without collecting any personally identifiable information. Our proposed approach can assist a movie recommendation system understand user preferences using the user's movie watch-time and watch history only. We conducted a comprehensive and comparative study on the performance of three deep reinforcement learning architectures, namely DQN, DDQN, and D3QN, on the same task. We observed that D3QN outperformed the other two architectures and achieved a precision of 0.880, recall of 0.805, and F1 score of 0.830. The results show that we can build a competitive movie recommendation system using unidentifiable data.
A safer approach to build recommendation systems on unidentifiable data
A safer approach to build recommendation systems on unidentifiable data
Kishor Datta Gupta
Adversarial Attacks and Defense
Adversarial Attacks and Defense
Adversarial Attacks and Defense
Kishor Datta Gupta
Presented in ICML2021 DMMLSys workshop
Who is responsible for adversarial defense
Who is responsible for adversarial defense
Kishor Datta Gupta
Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial Attacks
Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial A...
Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial A...
Kishor Datta Gupta
Zero Shot Learning using word2vec and attribute based image classification
Zero shot learning
Zero shot learning
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.
Using Negative Detectors for Identifying Adversarial Data Manipulation in Mac...
Using Negative Detectors for Identifying Adversarial Data Manipulation in Mac...
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
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Kishor Datta Gupta
Machine Learning Methods in Computer Security. How ML methods can solve computer security problems.
Machine learning in computer security
Machine learning in computer security
Kishor Datta Gupta
Policy-Based reinforcement Learning for time series Anomaly detection
Policy Based reinforcement Learning for time series Anomaly detection
Policy Based reinforcement Learning for time series Anomaly detection
Kishor Datta Gupta
Intrusion detection system
Cyber intrusion
Cyber intrusion
Kishor Datta Gupta
Newspaper reports are a daily information tank for the majority of the world. We rely on newspapers as a primary source of information. In this research, we introduce a collection of 1050 news report dataset on COVID-19 from two different countries and used Natural Language Processing techniques to extract knowledge about the virus, including the number of COVID-cases, trending topics per month, sentiment analysis, etc. Moreover, we compared how the virus spreads and impacts a developed country and a developing country. Our curated dataset can be used in various socio-economical studies to understand news media's effect on public awareness Conference: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)At: Lasvegas, NV, USA
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
Different representation space for MNIST digit
Different representation space for MNIST digit
Different representation space for MNIST digit
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
"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
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. https://www.researchgate.net/publication/344463103_Applicability_issues_of_Evasion-Based_Adversarial_Attacks_and_Mitigation_Techniques
Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Tech...
Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Tech...
Kishor Datta Gupta
Modern deep learning models for the computer vision domain are vulnerable against adversarial attacks. Image prepossessing technique based defense against malicious input is currently considered obsolete as this defense is not effective against all types of attacks. The advanced adaptive attack can easily defeat pre-processing based defenses. In this paper, we proposed a framework that will generate a set of image processing sequences (several image processing techniques in a series). We randomly select a set of Image processing technique sequences (IPTS) dynamically to answer the obscurity question in testing time. This paper outlines methodology utilizing varied datasets examined with various adversarial data manipulations. For specific attack types and dataset, it produces unique IPTS. The outcome of our empirical experiments shows that the method can efficiently employ as processing for any machine learning models. The research also showed that our process works against adaptive attacks as we are using a non-deterministic set of IPTS for each adversarial input.
Adversarial Input Detection Using Image Processing Techniques (IPT)
Adversarial Input Detection Using Image Processing Techniques (IPT)
Kishor Datta Gupta
Basic digital image concept
Basic digital image concept
Basic digital image concept
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.
An empirical study on algorithmic bias (aiml compsac2020)
An empirical study on algorithmic bias (aiml compsac2020)
Kishor Datta Gupta
Hybrid pow-pos-based system against majority attack-in-cryptocurrency system compact version
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
Hashing is vulnerable to quantum attack. So keep password text hidden using hash in quantum computer era is not viable. Shamir's secret sharing can be used for authentication purpose as a alternative of hashing
Shamir secret sharing: Alternative of hashing for authentication
Shamir secret sharing: Alternative of hashing for authentication
Kishor Datta Gupta
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this project to find the optimal solution of energy scheduling throughout a day, simultaneously using the concept of network reconfiguration. In this research paper, we have applied a genetic algorithm approach, in order to optimize dispatching power with reconfiguring the network and scheduling the power sources. Our proposed methods shows that, it is possible to get 1MW less line lose compared to general condition.
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
Kishor Datta Gupta
Ransomware attacks in recent years have proved expensive due to significant damages and obstructions these caused in various sectors such as health, insurance, business, and education. Several malware detection methods have been proposed to uncover different malware families, but the problem remained unsolved due to the continuously evolving malware. In this work, we proposed a multi-level big data mining framework combining Reverse engineering, Natural Language Processing(NLP) and Machine Learning(ML) approaches. The framework analyzes the ransomware at different levels (i.e., Dynamic link library, function call and assembly instruction level) via different supervised ML algorithms. Apache Spark was employed for faster processing of large generated feature set. Portable Executable (PE) parser and Objectdump tool of Linux system were used to get the raw data from the ransomware and normal binaries that were processed further using our custom-built NLP processing. The n-gram probabilities, term-frequency and inverse document frequency (TF-IDF) were used to generate the final feature sets. Experiments were performed with different ‘N’ values of n-gram language model that shows that the ransomware detection accuracy is inversely proportional to the value of N. Among the five chosen supervised classifiers, Logistic regression outperformed others with a detection rate of 98.59% for generated TF-IDFs trigrams at combined multi-level, which is an improved accuracy compared to individual levels.
Multi level ransomware analysis MALCON 2019 conference
Multi level ransomware analysis MALCON 2019 conference
Kishor Datta Gupta
An Application of Combinatorial Methods for Explainability in Artificial Intelligence and Machine Learning . Tradeoff between AI accuracy and explainability. If a combination of factor values occurs in a passing test, then clearly it did not trigger the failure. combinations that occur only in failing tests are those that are considered in narrowing down the set of suspect combinations
COMXAI A tool to explain AI USING FAULT LOCATION
COMXAI A tool to explain AI USING FAULT LOCATION
Kishor Datta Gupta
Time expired ledger for File access blockchain
Time expired ledger for File access blockchain
Time expired ledger for File access blockchain
Kishor Datta Gupta
Performance of Spark in Malicious website classification using machine learning
BigData Computing For WebSite Classifier
BigData Computing For WebSite Classifier
Kishor Datta Gupta
Log files are generated in many different formats by a plethora of devices and software. The proper analysis of these files can lead to useful information about various aspects of each system. Cloud computing appears to be suitable for this type of analysis, as it is capable to manage the high production rate, the large size and the diversity of log files. In this paper we investigated log file analysis with the cloud computational frameworks ApacheTM Hadoop R and Apache SparkTM. We developed realistic log file analysis applications in both frameworks and we performed SQL-type queries in real Apache Web Server log files. Various experiments were performed with different parameters in order to study and compare the performance of the two frameworks.
Performance evaluation of cloud-based log file analysis with Apache Hadoop an...
Performance evaluation of cloud-based log file analysis with Apache Hadoop an...
Kishor Datta Gupta
“There is an unconscious aspect to an author’s style that cannot be consciously manipulated but which possesses quantifiable and distinctive features” Plagiarism Checker. Forensic analysis. Author Identification. Software Code analysis. Genre classification. Historical Study. Context Understanding Music Lyrics Character Based Feature Lexical Feature Syntactic Feature Content Specific Feature Readability Feature Information Retrieval feature
Stylometry In Authentication
Stylometry In Authentication
Kishor Datta Gupta
Roadmap to Membership of Royal Institute of Chartered Surveyors الطريق إلى عضوية المعهد الملكي البريطاني للمساحين المعتمدين
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Roadmap to Membership of RICS - Pathways and Routes
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—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
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Kishor Datta Gupta
Machine Learning Methods in Computer Security. How ML methods can solve computer security problems.
Machine learning in computer security
Machine learning in computer security
Kishor Datta Gupta
Policy-Based reinforcement Learning for time series Anomaly detection
Policy Based reinforcement Learning for time series Anomaly detection
Policy Based reinforcement Learning for time series Anomaly detection
Kishor Datta Gupta
Intrusion detection system
Cyber intrusion
Cyber intrusion
Kishor Datta Gupta
Newspaper reports are a daily information tank for the majority of the world. We rely on newspapers as a primary source of information. In this research, we introduce a collection of 1050 news report dataset on COVID-19 from two different countries and used Natural Language Processing techniques to extract knowledge about the virus, including the number of COVID-cases, trending topics per month, sentiment analysis, etc. Moreover, we compared how the virus spreads and impacts a developed country and a developing country. Our curated dataset can be used in various socio-economical studies to understand news media's effect on public awareness Conference: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)At: Lasvegas, NV, USA
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
Different representation space for MNIST digit
Different representation space for MNIST digit
Different representation space for MNIST digit
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
"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
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. https://www.researchgate.net/publication/344463103_Applicability_issues_of_Evasion-Based_Adversarial_Attacks_and_Mitigation_Techniques
Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Tech...
Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Tech...
Kishor Datta Gupta
Modern deep learning models for the computer vision domain are vulnerable against adversarial attacks. Image prepossessing technique based defense against malicious input is currently considered obsolete as this defense is not effective against all types of attacks. The advanced adaptive attack can easily defeat pre-processing based defenses. In this paper, we proposed a framework that will generate a set of image processing sequences (several image processing techniques in a series). We randomly select a set of Image processing technique sequences (IPTS) dynamically to answer the obscurity question in testing time. This paper outlines methodology utilizing varied datasets examined with various adversarial data manipulations. For specific attack types and dataset, it produces unique IPTS. The outcome of our empirical experiments shows that the method can efficiently employ as processing for any machine learning models. The research also showed that our process works against adaptive attacks as we are using a non-deterministic set of IPTS for each adversarial input.
Adversarial Input Detection Using Image Processing Techniques (IPT)
Adversarial Input Detection Using Image Processing Techniques (IPT)
Kishor Datta Gupta
Basic digital image concept
Basic digital image concept
Basic digital image concept
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.
An empirical study on algorithmic bias (aiml compsac2020)
An empirical study on algorithmic bias (aiml compsac2020)
Kishor Datta Gupta
Hybrid pow-pos-based system against majority attack-in-cryptocurrency system compact version
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
Hashing is vulnerable to quantum attack. So keep password text hidden using hash in quantum computer era is not viable. Shamir's secret sharing can be used for authentication purpose as a alternative of hashing
Shamir secret sharing: Alternative of hashing for authentication
Shamir secret sharing: Alternative of hashing for authentication
Kishor Datta Gupta
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this project to find the optimal solution of energy scheduling throughout a day, simultaneously using the concept of network reconfiguration. In this research paper, we have applied a genetic algorithm approach, in order to optimize dispatching power with reconfiguring the network and scheduling the power sources. Our proposed methods shows that, it is possible to get 1MW less line lose compared to general condition.
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
Kishor Datta Gupta
Ransomware attacks in recent years have proved expensive due to significant damages and obstructions these caused in various sectors such as health, insurance, business, and education. Several malware detection methods have been proposed to uncover different malware families, but the problem remained unsolved due to the continuously evolving malware. In this work, we proposed a multi-level big data mining framework combining Reverse engineering, Natural Language Processing(NLP) and Machine Learning(ML) approaches. The framework analyzes the ransomware at different levels (i.e., Dynamic link library, function call and assembly instruction level) via different supervised ML algorithms. Apache Spark was employed for faster processing of large generated feature set. Portable Executable (PE) parser and Objectdump tool of Linux system were used to get the raw data from the ransomware and normal binaries that were processed further using our custom-built NLP processing. The n-gram probabilities, term-frequency and inverse document frequency (TF-IDF) were used to generate the final feature sets. Experiments were performed with different ‘N’ values of n-gram language model that shows that the ransomware detection accuracy is inversely proportional to the value of N. Among the five chosen supervised classifiers, Logistic regression outperformed others with a detection rate of 98.59% for generated TF-IDFs trigrams at combined multi-level, which is an improved accuracy compared to individual levels.
Multi level ransomware analysis MALCON 2019 conference
Multi level ransomware analysis MALCON 2019 conference
Kishor Datta Gupta
An Application of Combinatorial Methods for Explainability in Artificial Intelligence and Machine Learning . Tradeoff between AI accuracy and explainability. If a combination of factor values occurs in a passing test, then clearly it did not trigger the failure. combinations that occur only in failing tests are those that are considered in narrowing down the set of suspect combinations
COMXAI A tool to explain AI USING FAULT LOCATION
COMXAI A tool to explain AI USING FAULT LOCATION
Kishor Datta Gupta
Time expired ledger for File access blockchain
Time expired ledger for File access blockchain
Time expired ledger for File access blockchain
Kishor Datta Gupta
Performance of Spark in Malicious website classification using machine learning
BigData Computing For WebSite Classifier
BigData Computing For WebSite Classifier
Kishor Datta Gupta
Log files are generated in many different formats by a plethora of devices and software. The proper analysis of these files can lead to useful information about various aspects of each system. Cloud computing appears to be suitable for this type of analysis, as it is capable to manage the high production rate, the large size and the diversity of log files. In this paper we investigated log file analysis with the cloud computational frameworks ApacheTM Hadoop R and Apache SparkTM. We developed realistic log file analysis applications in both frameworks and we performed SQL-type queries in real Apache Web Server log files. Various experiments were performed with different parameters in order to study and compare the performance of the two frameworks.
Performance evaluation of cloud-based log file analysis with Apache Hadoop an...
Performance evaluation of cloud-based log file analysis with Apache Hadoop an...
Kishor Datta Gupta
“There is an unconscious aspect to an author’s style that cannot be consciously manipulated but which possesses quantifiable and distinctive features” Plagiarism Checker. Forensic analysis. Author Identification. Software Code analysis. Genre classification. Historical Study. Context Understanding Music Lyrics Character Based Feature Lexical Feature Syntactic Feature Content Specific Feature Readability Feature Information Retrieval feature
Stylometry In Authentication
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Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Machine learning in computer security
Machine learning in computer security
Policy Based reinforcement Learning for time series Anomaly detection
Policy Based reinforcement Learning for time series Anomaly detection
Cyber intrusion
Cyber intrusion
understanding the pandemic through mining covid news using natural language p...
understanding the pandemic through mining covid news using natural language p...
Different representation space for MNIST digit
Different representation space for MNIST digit
"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...
Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Tech...
Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Tech...
Adversarial Input Detection Using Image Processing Techniques (IPT)
Adversarial Input Detection Using Image Processing Techniques (IPT)
Basic digital image concept
Basic digital image concept
An empirical study on algorithmic bias (aiml compsac2020)
An empirical study on algorithmic bias (aiml compsac2020)
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
Shamir secret sharing: Alternative of hashing for authentication
Shamir secret sharing: Alternative of hashing for authentication
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
Multi level ransomware analysis MALCON 2019 conference
Multi level ransomware analysis MALCON 2019 conference
COMXAI A tool to explain AI USING FAULT LOCATION
COMXAI A tool to explain AI USING FAULT LOCATION
Time expired ledger for File access blockchain
Time expired ledger for File access blockchain
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BigData Computing For WebSite Classifier
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Performance evaluation of cloud-based log file analysis with Apache Hadoop an...
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Roadmap to Membership of Royal Institute of Chartered Surveyors الطريق إلى عضوية المعهد الملكي البريطاني للمساحين المعتمدين
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Structural Analysis and Design of Foundations: A Comprehensive Handbook for Students and Professionals. Unlock the potential of foundation design with Dr. Costas Sachpazis’s enlightening handbook, a meticulously crafted guide poised to become an indispensable resource for both budding and seasoned civil engineers. This comprehensive manual illuminates the theoretical and practical aspects of structural analysis and design across various types of foundations and retaining walls. Within these pages, Dr. Sachpazis distills complex engineering principles into digestible, step-by-step processes, enhanced by detailed diagrams, case studies, and real-world examples that bridge the gap between academic study and professional application. From soil mechanics and load calculations to innovative design techniques and sustainability considerations, this book covers a vast landscape of structural engineering. Key Features: • In-Depth Analysis and Design: Explore thorough explanations of both shallow and deep foundation designs, supported by case studies that demonstrate their practical implementations. • Practical Guides: Benefit from detailed guides on site investigation, bearing capacity calculations, and settlement analysis, ensuring designs are both robust and reliable. • Innovative Techniques: Discover the latest advancements in foundation technology and retaining wall design, preparing you for future trends in civil engineering. • Educational Tools: Utilize this handbook as an educational tool, perfect for both classroom learning and professional development. Whether you're a student eager to learn the fundamentals or a professional seeking to deepen your expertise, Dr. Sachpazis’s handbook is designed to support and inspire excellence in the field of structural engineering. Embrace this opportunity to enhance your skills and contribute to building safer, more efficient structures.
Structural Analysis and Design of Foundations: A Comprehensive Handbook for S...
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International Journal of Intelligent Systems and Applications in Engineering (IJISAE) is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. IJISAE, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.
Call for Papers - International Journal of Intelligent Systems and Applicatio...
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Double rodded leveling 1 pdf activity 01
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Presentation ppt about the generative AI technology.
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There are common misconceptions among design engineers regarding pipe stiffness, pipe deflection, impact strength, abrasion, and design life when comparing PVC and fiberglass (FRP) sewer pipes. When correct evaluations are done, PVC is the product of choice.
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STEAM NOZZLES AND TURBINES Flow of steam through nozzles, shapes of nozzles, effect of friction, critical pressure ratio, supersaturated flow - impulse and reaction principles, velocity diagram, work done and efficiency – types of compounding - governors. AIR COMPRESSORS Classification - working principle - type of compressors, work of compression with and without clearance - volumetric efficiency - isothermal and isentropic efficiency of reciprocating compressors - multistage air compressor with inter cooling.
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N-Grade deals with the maintenance of university, department, faculty, student information within the university. N-Grade is an automation system, which is used to store the department, faculty, student, courses and information of a university. Starting from registration of a new student in the university, it maintains all the details regarding the attendance and marks of the students. The project deals with retrieval of information through an INTRANET based campus wide portal. It collects related information from all the departments of an organization and maintains files, which are used to generate reports in various forms to measure individual and overall performance of the students.
University management System project report..pdf
University management System project report..pdf
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Welcome to the April edition of WIPAC Monthly, the magazine brought to you by Water Industry Process Automation & Control. In this month's edition, along with the latest news from the industry we have articles on: The use of artificial intelligence and self-service platforms to improve water sustainability A feature article on measuring wastewater spills An article on the National Underground Asset Register Have a good month, Oliver
Water Industry Process Automation & Control Monthly - April 2024
Water Industry Process Automation & Control Monthly - April 2024
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Roadmap to Membership of RICS - Pathways and Routes
Roadmap to Membership of RICS - Pathways and Routes
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Structural Analysis and Design of Foundations: A Comprehensive Handbook for S...
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University management System project report..pdf
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Clustering report
1.
Clustering Adversarial vs Non
Adversarial Kishor Datta Gupta
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Data Dataset MNIST 10000 Clean
images from 10 class 10000 FGSM attack method from 10 class Data extraction method: PCA Clustering Method: • K-Means • LDA • T-SNE
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Clean Adversarial
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Clean Adversarial
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Clean Adversarial
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