Browsing by Author "Tripathi, A."

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    A reinforcement learning and recurrent neural network based dynamic user modeling system
    (Institute of Electrical and Electronics Engineers Inc., 2018) Tripathi, A.; Ashwin, T.S.; Guddeti, R.M.
    With the exponential growth in areas of machine intelligence, the world has witnessed promising solutions to the personalized content recommendation. The ability of interactive learning agents to take optimal decisions in dynamic environments has been very well conceptualized and proven by Reinforcement Learning (RL). The learning characteristics of Deep-Bidirectional Recurrent Neural Networks (DBRNN) in both positive and negative time directions has shown exceptional performance as generative models to generate sequential data in supervised learning tasks. In this paper, we harness the potential of the said two techniques and strive to create personalized video recommendation through emotional intelligence by presenting a novel context-Aware collaborative filtering approach where intensity of users' spontaneous non-verbal emotional response towards recommended video is captured through system-interactions and facial expression analysis for decision-making and video corpus evolution with real-Time data streams. We take into account a user's dynamic nature in the formulation of optimal policies, by framing up an RL-scenario with an off-policy (Q-Learning) algorithm for temporal-difference learning, which is used to train DBRNN to learn contextual patterns and generate new video sequences for the recommendation. Evaluation of our system with real users for a month shows that our approach outperforms state-of-The-Art methods and models a user's emotional preferences very well with stable convergence. © 2018 IEEE.
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    Computationally efficient fault tolerant ANTS
    (2016) Tripathi, A.; Maheshwari, A.; Chandrasekaran, K.
    In this paper, we formulate a method to utilize n mobile agents to solve a variant of Ants Nearby Treasure Search problem (ANTS), where an adversary can place treasure at any cell at a distance D from the origin. We devise a method which finds the treasure with the time complex-ity of O(D + D2=n + Df) where D is the Manhattan dis-tance of the treasure from the source and f is the maximum number of failures such that f 2 o(n). The algorithm is specially designed to reduce computation complexity of the distributed system as a whole by efficiently handling fail-ures and also, introducing the elements of parallelism with respect to handling failures. Using our algorithm, we bring down the computation cost/complexity of the system by an order of n, when failures occur, where n is the total number of ants. ANTS problem utilizes the multi-Agent system with self-organization and steering based on a control mechanism which is analogous to the problem of discovering resources that are available to the distributed system. � 2016 ACM.
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    Computationally efficient fault tolerant ANTS
    (Association for Computing Machinery acmhelp@acm.org, 2016) Tripathi, A.; Maheshwari, A.; Chandrasekaran, K.
    In this paper, we formulate a method to utilize n mobile agents to solve a variant of Ants Nearby Treasure Search problem (ANTS), where an adversary can place treasure at any cell at a distance D from the origin. We devise a method which finds the treasure with the time complex-ity of O(D + D2=n + Df) where D is the Manhattan dis-tance of the treasure from the source and f is the maximum number of failures such that f 2 o(n). The algorithm is specially designed to reduce computation complexity of the distributed system as a whole by efficiently handling fail-ures and also, introducing the elements of parallelism with respect to handling failures. Using our algorithm, we bring down the computation cost/complexity of the system by an order of n, when failures occur, where n is the total number of ants. ANTS problem utilizes the multi-Agent system with self-organization and steering based on a control mechanism which is analogous to the problem of discovering resources that are available to the distributed system. © 2016 ACM.
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    Electric Power Systems for Non-Electrical Engineers
    (CRC Press, 2024) Tripathi, A.
    This book explains the electrical power systems for non-electrical engineers and includes topics like electrical energy systems, electrical power systems structure, single-phase AC circuit fundamentals and three-phase systems, power system modeling, power system representation, power system operation, power flow analysis, economic operation of power systems, power system fault analysis, power system protection fundamentals, and so forth. Examples have been provided to clarify the description, and review questions are provided at the end of each chapter. Provides a simplified description of fundamentals of electrical energy systems and structure of electrical power systems for non-electrical engineers. Gives a detailed description of AC circuit fundamentals and three-phase systems. Describes power system modeling and power system representation. Covers power system operation, power flow analysis, and fundamentals of economic operation of power systems. Discusses power system fault analysis and fundamentals of power system protection with examples, and also includes renewable energy systems. This book has been aimed at senior undergraduate and graduate students of non-electrical engineering background. © 2025 Anup Tripathi.
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    EmoWare: A context-aware framework for personalized video recommendation using affective video sequences
    (Institute of Electrical and Electronics Engineers Inc., 2019) Tripathi, A.; Ashwin, T.S.; Guddeti, R.M.R.
    With the exponential growth in areas of machine intelligence, the world has witnessed promising solutions to the personalized content recommendation. The ability of interactive learning agents to make optimal decisions in dynamic environments has been proven and very well conceptualized by reinforcement learning (RL). The learning characteristics of deep-bidirectional recurrent neural networks (DBRNN) in both positive and negative time directions has shown exceptional performance as generative models to generate sequential data in supervised learning tasks. In this paper, we harness the potential of the said two techniques and propose EmoWare (emotion-aware), a personalized, emotionally intelligent video recommendation engine, employing a novel context-aware collaborative filtering approach, where the intensity of users' spontaneous non-verbal emotional response toward the recommended video is captured through interactions and facial expressions analysis for decision-making and video corpus evolution with real-time feedback streams. To account for users' multidimensional nature in the formulation of optimal policies, RL-scenarios are enrolled using on-policy (SARSA) and off-policy (Q-learning) temporal-difference learning techniques, which are used to train DBRNN to learn contextual patterns and to generate new video sequences for the recommendation. System evaluation for a month with real users shows that the EmoWare outperforms the state-of-the-art methods and models users' emotional preferences very well with stable convergence. © 2013 IEEE.
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    Normalized videosnapping: A non-linear video synchronization approach
    (2018) Tripathi, A.; Changmai, B.; Habib, S.; Chittaragi, N.B.; Koolagudi, S.G.
    Video synchronization is the task of content-based alignment of two or more videos depicting the same event with spatial variations or in the same object with temporal changes. Video synchronization is one of the most fundamental tasks when it comes to manipulations with temporally or spatially multi-perspective video-shots. In this paper, a model is proposed to deal with the synchronization problem and efficiently tackles issues arising during synchronizing two videos. Here, videos are dealt, at the frame level with features from each frame forming the basis of alignment. Features are matched and mapped to generate a cost matrix of similarities among the frames of the videos in concern. A modified version of Djikstra's algorithm that yields an optimal path through the matrix is applied. Through an optimal path, events are grouped into adjacent regions following which temporal warpings are introduced into the videos to achieve the best possible alignment among them. The model has proven to be efficient and compatible with all classes of quality levels of videos. � 2017 IEEE.
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    Normalized videosnapping: A non-linear video synchronization approach
    (Institute of Electrical and Electronics Engineers Inc., 2017) Tripathi, A.; Changmai, B.; Habib, S.; Chittaragi, N.B.; Koolagudi, S.G.
    Video synchronization is the task of content-based alignment of two or more videos depicting the same event with spatial variations or in the same object with temporal changes. Video synchronization is one of the most fundamental tasks when it comes to manipulations with temporally or spatially multi-perspective video-shots. In this paper, a model is proposed to deal with the synchronization problem and efficiently tackles issues arising during synchronizing two videos. Here, videos are dealt, at the frame level with features from each frame forming the basis of alignment. Features are matched and mapped to generate a cost matrix of similarities among the frames of the videos in concern. A modified version of Djikstra's algorithm that yields an optimal path through the matrix is applied. Through an optimal path, events are grouped into adjacent regions following which temporal warpings are introduced into the videos to achieve the best possible alignment among them. The model has proven to be efficient and compatible with all classes of quality levels of videos. © 2017 IEEE.
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    A reinforcement learning and recurrent neural network based dynamic user modeling system
    (2018) Tripathi, A.; Ashwin, T.S.; Ram Mohana Reddy, Guddeti
    With the exponential growth in areas of machine intelligence, the world has witnessed promising solutions to the personalized content recommendation. The ability of interactive learning agents to take optimal decisions in dynamic environments has been very well conceptualized and proven by Reinforcement Learning (RL). The learning characteristics of Deep-Bidirectional Recurrent Neural Networks (DBRNN) in both positive and negative time directions has shown exceptional performance as generative models to generate sequential data in supervised learning tasks. In this paper, we harness the potential of the said two techniques and strive to create personalized video recommendation through emotional intelligence by presenting a novel context-Aware collaborative filtering approach where intensity of users' spontaneous non-verbal emotional response towards recommended video is captured through system-interactions and facial expression analysis for decision-making and video corpus evolution with real-Time data streams. We take into account a user's dynamic nature in the formulation of optimal policies, by framing up an RL-scenario with an off-policy (Q-Learning) algorithm for temporal-difference learning, which is used to train DBRNN to learn contextual patterns and generate new video sequences for the recommendation. Evaluation of our system with real users for a month shows that our approach outperforms state-of-The-Art methods and models a user's emotional preferences very well with stable convergence. � 2018 IEEE.
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    Role of intensity of emotions for effective personalized video recommendation: A reinforcement learning approach
    (Springer Verlag service@springer.de, 2018) Tripathi, A.; Manasa, D.G.; Rakshitha, K.; Ashwin, T.S.; Reddy, G.
    Development of artificially intelligent agents in video recommendation systems over past decade has been an active research area. In this paper, we have presented a novel hybrid approach (combining collaborative as well as content-based filtering) to create an agent which targets the intensity of emotional content present in a video for recommendation. Since cognitive preferences of a user in real world are always in a dynamic state, tracking user behavior in real time as well as the general cognitive preferences of the users toward different emotions is a key parameter for recommendation. The proposed system monitors the user interactions with the recommended video from its user interface and web camera to learn the criterion of decision-making in real time through reinforcement learning. To evaluate the proposed system, we have created our own UI, collected videos from YouTube, and applied Q-learning to train our system to effectively adapt user preferences. © Springer Nature Singapore Pte Ltd. 2018