Browsing by Author "Jain, S."

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AI Technology for NoC Performance Evaluation
(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.; Hazarika, P.; Kale, P.; Jain, S.
An on-chip network has become a powerful platform for solving complex and large-scale computation problems in the present decade. However, the performance of bus-based architectures, including an increasing number of IP cores in systems-on-chip (SoCs), does not meet the requirements of lower latencies and higher bandwidth for many applications. A network-on-chip (NoC) has become a prevalent solution to overcome the limitations. Performance analysis of NoC's is essential for its architectural design. NoC simulators traditionally investigate performance despite they are slow with varying architectural sizes. This work proposes a machine learning-based framework that evaluates NoC performance quickly. The proposed framework uses the linear regression method to predict different performance metrics by learning the trained dataset speedily and accurately. Varying architectural parameters conduct thorough experiments on a set of mesh NoCs. The experiments' highlights include the network latency, hop count, maximum switch, and channel power consumption as 30-80 cycles, 2-11, $25\mu \text{W}$ , and $240\mu \text{W}$ , respectively. Further, the proposed framework achieves accuracy up to 94% and speedup of up to $2228\times $. © 2004-2012 IEEE.
Automatic Abnormality Detection inÂ Musculoskeletal Radiographs Using Ensemble ofÂ Pre-trained Networks
(Springer Science and Business Media Deutschland GmbH, 2023) Verma, R.; Jain, S.; Saritha, S.K.; Dodia, S.
Musculoskeletal disability (MSDs) defined as the injuries that affect the movement or musculoskeletal system of the human body. Over the worldwide, it is the second most cause of physical disability. Musculoskeletal disability worsens over time and can result in long-term discomfort and severe disability. As a result, early detection and diagnosis of these anomalies is essential. But the diagnosis process is very time consuming, error prone and required diagnostic professional. Deep learning algorithms have recently been applied in medical imaging that provides a robust platform with very reliable outcomes. The development of Computer Aided Detection (CAD) system extensively speed up the diagnosis process. In this paper, a weighted ensemble model has been proposed, which is the combination of three pre-trained models (DenseNet169, MobileNet, and XceptionNet). The weighted ensemble model is tested on MURA dataset, a large public dataset provided by Stanford ML Group. Our model achieved a cohenâ€™s kappa score 0.739 with precision of 0.885 and recall of 0.854, which is higher than many existing approaches such as densenet169 and ensemble200 model. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Detection and analysis model for grammatical facial expressions in sign language
(2016) Bhuvan, M.S.; Rao, D.V.; Jain, S.; Ashwin, T.S.; Ram Mohana Reddy, Guddeti; Kulgod, S.P.
The proposed research explores a relatively new area of expression detection through facial points in a sign language to enhance the computer interaction with the deaf and hard of hearing. The research mainly focuses on facial points collected from Kinect as basis for expression detection as opposed to numerous gesture based studies on sign language. This helps in deploying the applications in smart phones as it is feasible to capture facial point easily rather than hand gestures. Exhaustive experimentation is carried out with ten different machine learning algorithms for detecting nine different types of expression modeled as different binary classification problem for each expression. This is done for user dependent model and user independent model scenarios. The optimal classifier for each expression is found to outperform the current state-of-the-art techniques and has ROC area greater than 0.95 for each expression. It is found that user independent model's performance is comparable to user dependent model, hence is suggested as it is easy and efficient to deploy in practical applications. Finally, the importance of each facial point in detecting each type of expression has been mined, which can be instrumental for future research and for various application using facial points as basis for decision making. � 2016 IEEE.
Detection and analysis model for grammatical facial expressions in sign language
(Institute of Electrical and Electronics Engineers Inc., 2016) Bhuvan, M.S.; Rao, D.V.; Jain, S.; Ashwin, T.S.; Guddeti, G.R.; Kulgod, S.P.
The proposed research explores a relatively new area of expression detection through facial points in a sign language to enhance the computer interaction with the deaf and hard of hearing. The research mainly focuses on facial points collected from Kinect as basis for expression detection as opposed to numerous gesture based studies on sign language. This helps in deploying the applications in smart phones as it is feasible to capture facial point easily rather than hand gestures. Exhaustive experimentation is carried out with ten different machine learning algorithms for detecting nine different types of expression modeled as different binary classification problem for each expression. This is done for user dependent model and user independent model scenarios. The optimal classifier for each expression is found to outperform the current state-of-the-art techniques and has ROC area greater than 0.95 for each expression. It is found that user independent model's performance is comparable to user dependent model, hence is suggested as it is easy and efficient to deploy in practical applications. Finally, the importance of each facial point in detecting each type of expression has been mined, which can be instrumental for future research and for various application using facial points as basis for decision making. Â© 2016 IEEE.
Effective Information Retrieval, Question Answering andÂ Abstractive Summarization onÂ Large-Scale Biomedical Document Corpora
(Springer Science and Business Media Deutschland GmbH, 2023) Shenoy, N.; Nayak, P.; Jain, S.; Kamath Sâ€¤, S.; Sugumaran, V.
During the COVID-19 pandemic, a concentrated effort was made to collate published literature on SARS-Cov-2 and other coronaviruses for the benefit of the medical community. One such initiative is the COVID-19 Open Research Dataset which contains over 400,000 published research articles. To expedite access to relevant information sources for health workers and researchers, it is vital to design effective information retrieval and information extraction systems. In this article, an IR approach leveraging transformer-based models to enable question-answering and abstractive summarization is presented. Various keyword-based and neural-network-based models are experimented with and incorporated to reduce the search space and determine relevant sentences from the vast corpus for ranked retrieval. For abstractive summarization, candidate sentences are determined using a combination of various standard scoring metrics. Finally, the summary and the user query are utilized for supporting question answering. The proposed model is evaluated based on standard metrics on the standard CovidQA dataset for both natural language and keyword queries. The proposed approach achieved promising performance for both query classes, while outperforming various unsupervised baselines. Â© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Evaluation of Machine Learning Frameworks on Bank Marketing and Higgs Datasets
(2015) Shashidhara, B.M.; Jain, S.; Rao, V.D.; Patil, N.; Raghavendra, G.S.
Big data is an emerging field with different datasets of various sizes are being analyzed for potential applications. In parallel, many frameworks are being introduced where these datasets can be fed into machine learning algorithms. Though some experiments have been done to compare different machine learning algorithms on different data, these experiments have not been tested out on different platforms. Our research aims to compare two selected machine learning algorithms on data sets of different sizes deployed on different platforms like Weka, Scikit-Learn and Apache Spark. They are evaluated based on Training time, Accuracy and Root mean squared error. This comparison helps us to decide what platform is best suited to work while applying computationally expensive selected machine learning algorithms on a particular size of data. Experiments suggested that Scikit-Learn would be optimal on data which can fit into memory. While working with huge, data Apache Spark would be optimal as it performs parallel computations by distributing the data over a cluster. Hence this study concludes that spark platform which has growing support for parallel implementation of machine learning algorithms could be optimal to analyze big data. � 2015 IEEE.
Evaluation of Machine Learning Frameworks on Bank Marketing and Higgs Datasets
(Institute of Electrical and Electronics Engineers Inc., 2015) Bhuvan, B.M.; Jain, S.; Rao, V.D.; Patil, N.; Raghavendra, G.S.
Big data is an emerging field with different datasets of various sizes are being analyzed for potential applications. In parallel, many frameworks are being introduced where these datasets can be fed into machine learning algorithms. Though some experiments have been done to compare different machine learning algorithms on different data, these experiments have not been tested out on different platforms. Our research aims to compare two selected machine learning algorithms on data sets of different sizes deployed on different platforms like Weka, Scikit-Learn and Apache Spark. They are evaluated based on Training time, Accuracy and Root mean squared error. This comparison helps us to decide what platform is best suited to work while applying computationally expensive selected machine learning algorithms on a particular size of data. Experiments suggested that Scikit-Learn would be optimal on data which can fit into memory. While working with huge, data Apache Spark would be optimal as it performs parallel computations by distributing the data over a cluster. Hence this study concludes that spark platform which has growing support for parallel implementation of machine learning algorithms could be optimal to analyze big data. Â© 2015 IEEE.
Impact of group norms in eliciting response in a goal driven virtual community
(2013) Jain, S.; Sinha, T.; Shah, A.; Sharma, C.; Rose, C.
With the proliferation of social media into our daily lives, online communities have become an important platform for collaborative learning and education. To connect users with varying knowledge levels and increase the net learning throughput, these communities often follow a question-answer based approach. Understanding what drives attention to help-seeking questions can reduce the amount of questions that go unnoticed or remain unanswered by the community. In this paper we discuss an important feature that affects the activity of the community, namely the community norms. We present a machine learning based trigger-driven feedback model that functions by (i) differentiating between help-seeking questions and follow-up posts - i.e. posts that are part of an ongoing discussion, and (ii) a dynamic intervention scheme to help improve question formulation. Our findings show that adhering to the community norms significantly increases the chance of eliciting a response.
Impact of group norms in eliciting response in a goal driven virtual community
(UHAMKA PRESS uhamkapress@yahoo.co.id, 2013) Jain, S.; Sinha, T.; Shah, A.; Sharma, C.; RosÃ©, C.
With the proliferation of social media into our daily lives, online communities have become an important platform for collaborative learning and education. To connect users with varying knowledge levels and increase the net learning throughput, these communities often follow a question-answer based approach. Understanding what drives attention to help-seeking questions can reduce the amount of questions that go unnoticed or remain unanswered by the community. In this paper we discuss an important feature that affects the activity of the community, namely the community norms. We present a machine learning based trigger-driven feedback model that functions by (i) differentiating between help-seeking questions and follow-up posts - i.e. posts that are part of an ongoing discussion, and (ii) a dynamic intervention scheme to help improve question formulation. Our findings show that adhering to the community norms significantly increases the chance of eliciting a response.
Industrial automation using internet of things
(IGI Global, 2019) Chandrasekaran, K.; Jain, S.
This chapter presents a comprehensive view of Industrial Automation using internet of things (IIoT). Advanced Industries are ushering in a new age of physical production backed by the information-based economy. The term Industrie 4.0 refers to the 4th paradigm shift in production, in which intelligent manufacturing technology is interconnected with physical machines. IIoT is basically a convergence of industrial systems with advanced, near-real-time computing and analytics, powered by low cost and low power sensing devices leveraging global internet connectivity. The key benefits of Industrial IoT systems are a) improved operational efficiency and productivity b) reduced maintenance costs c) improved asset utilization, monitoring and maintenance d) development of new business models e) product innovation and f) enhanced safety. Key parameters that impact Industrial Automation are a) Security b) Data Integrity c) Interoperability d) Latency e) Scalability, Reliability, and Availability f) Fault tolerance and Safety, and g) Maintainability, Serviceability, and Programmability. © 2020 by IGI Global. All rights reserved.
Industrial Automation Using Internet of Things
(IGI Global, 2021) Jain, S.; Chandrasekaran, K.
This chapter presents a comprehensive view of Industrial Automation using internet of things (IIoT). Advanced Industries are ushering in a new age of physical production backed by the information-based economy. The term Industrie 4.0 refers to the 4th paradigm shift in production, in which intelligent manufacturing technology is interconnected with physical machines. IIoT is basically a convergence of industrial systems with advanced, near-real-time computing and analytics, powered by low cost and low power sensing devices leveraging global internet connectivity. The key benefits of Industrial IoT systems are a) improved operational efficiency and productivity b) reduced maintenance costs c) improved asset utilization, monitoring and maintenance d) development of new business models e) product innovation and f) enhanced safety. Key parameters that impact Industrial Automation are a) Security b) Data Integrity c) Interoperability d) Latency e) Scalability, Reliability, and Availability f) Fault tolerance and Safety, and g) Maintainability, Serviceability, and Programmability. © 2022 by IGI Global. All rights reserved.
Investigating the "wisdom of crowds" at scale
(2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Investigating the "wisdom of crowds" at scale
(Association for Computing Machinery, Inc acmhelp@acm.org, 2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Layer based 3D clipping
(2016) Kedia, Y.; Hendre, A.; Jain, S.; Afroz, F.; Koolagudi, S.G.
In this paper, we propose an unconventional layer based clipping algorithm for 3D regions. In computer graphics, clipping is used to select the required part of a graphical object, cut it out from the object and display it separately. The proposed algorithm is not based on any other algorithm generally used for clipping in computer graphics and has a much better time efficiency than the other clipping algorithms available. The 3D space i.e. a cuboid is clipped w.r.t. a rectangular clipping window. The novelty of the algorithm is that 2D regions are being clipped down to the dimensions of the intersection region and then varied along the depth(z-axis) to get the volume of intersection. The algorithm has been implemented for both unrotated and rotated cuboids. The proposed algorithm can have massive applications in any field that requires layer-wise imaging of 3D spaces such as 3D printing, medical imaging, modelling, etc. given the simplicity of its implementation. � 2015 IEEE.
Layer based 3D clipping
(Institute of Electrical and Electronics Engineers Inc., 2016) Kedia, Y.; Hendre, A.; Jain, S.; Afroz, F.; Koolagudi, S.G.
In this paper, we propose an unconventional layer based clipping algorithm for 3D regions. In computer graphics, clipping is used to select the required part of a graphical object, cut it out from the object and display it separately. The proposed algorithm is not based on any other algorithm generally used for clipping in computer graphics and has a much better time efficiency than the other clipping algorithms available. The 3D space i.e. a cuboid is clipped w.r.t. a rectangular clipping window. The novelty of the algorithm is that 2D regions are being clipped down to the dimensions of the intersection region and then varied along the depth(z-axis) to get the volume of intersection. The algorithm has been implemented for both unrotated and rotated cuboids. The proposed algorithm can have massive applications in any field that requires layer-wise imaging of 3D spaces such as 3D printing, medical imaging, modelling, etc. given the simplicity of its implementation. Â© 2015 IEEE.
MechAnalyzer: Software to Teach Kinematics Concepts Related to Cams, Gears, and Instantaneous Center
(Springer, 2021) Dikshithaa, R.; Jain, S.; Swaminathan, J.; Chittawadigi, R.G.; Saha, S.K.
Theory of Machines and Mechanisms is the foundation for the worldâ€™s race toward automation. Simple mechanisms, cams, and gears find applications in all such automated systems. Therefore, a thorough understanding of these basics is quite essential. The authors are a part of the development team of MechAnalyzer, a software aimed at easing the teaching and learning of concepts related to mechanisms. In this paper, several new modules are reported. Cam module lets user select properties of a cam-follower mechanism and the steps required to draw cam profile is animated and shown to the user. Similarly, concepts related to gear profiles and meshing of gears are illustrated in gears module. Another module is presented which lets user understand the velocity analysis of planar mechanisms using the Instantaneous Center (IC) method. All these modules and the earlier set of modules to teach kinematics of mechanisms are readily available for free at http://www.roboanalyzer.com/mechanalyzer.html. Â© 2021, Springer Nature Singapore Pte Ltd.
Mining closed colossal frequent patterns from high-dimensional dataset: Serial versus parallel framework
(2018) Sureshan, S.; Penumacha, A.; Jain, S.; Vanahalli, M.; Patil, N.
Mining colossal patterns is one of the budding fields with a lot of applications, especially in the field of bioinformatics and genetics. Gene sequences contain inherent information. Mining colossal patterns in such sequences can further help in their study and improve prediction accuracy. The increase in average transaction length reduces the efficiency and effectiveness of existing closed frequent pattern mining algorithm. The traditional algorithms expend most of the running time in mining huge amount of minute and midsize patterns which do not enclose valuable information. The recent research focused on mining large cardinality patterns called as colossal patterns which possess valuable information. A novel parallel algorithm has been proposed to extract the closed colossal frequent patterns from high-dimensional datasets. The algorithm has been implemented on Hadoop framework to exploit its inherent distributed parallelism using MapReduce programming model. The experiment results highlight that the proposed parallel algorithm on Hadoop framework gives an efficient performance in terms of execution time compared to the existing algorithms. � Springer Nature Singapore Pte Ltd. 2018.
Mining closed colossal frequent patterns from high-dimensional dataset: Serial versus parallel framework
(Springer Verlag service@springer.de, 2018) Sureshan, S.; Penumacha, A.; Jain, S.; Vanahalli, M.; Patil, N.
Mining colossal patterns is one of the budding fields with a lot of applications, especially in the field of bioinformatics and genetics. Gene sequences contain inherent information. Mining colossal patterns in such sequences can further help in their study and improve prediction accuracy. The increase in average transaction length reduces the efficiency and effectiveness of existing closed frequent pattern mining algorithm. The traditional algorithms expend most of the running time in mining huge amount of minute and midsize patterns which do not enclose valuable information. The recent research focused on mining large cardinality patterns called as colossal patterns which possess valuable information. A novel parallel algorithm has been proposed to extract the closed colossal frequent patterns from high-dimensional datasets. The algorithm has been implemented on Hadoop framework to exploit its inherent distributed parallelism using MapReduce programming model. The experiment results highlight that the proposed parallel algorithm on Hadoop framework gives an efficient performance in terms of execution time compared to the existing algorithms. Â© Springer Nature Singapore Pte Ltd. 2018.
Mitigating Man-in-the-Middle Attack in Digital Signature
(Institute of Electrical and Electronics Engineers Inc., 2020) Jain, S.; Sharma, S.; Chandavarkar, B.R.
We all are living in the digital era, where the maximum of the information is available online. The digital world has made the transfer of information easy and provides the basic needs of security like authentication, integrity, nonrepudiation, etc. But, with the improvement in security, cyber-attacks have also increased. Security researchers have provided many techniques to prevent these cyber-attacks; one is a Digital Signature (DS). The digital signature uses cryptographic key pairs (public and private) to provide the message's integrity and verify the sender's identity. The private key used in the digital signature is confidential; if attackers find it by using various techniques, then this can result in an attack. This paper presents a brief introduction about the digital signature and how it is vulnerable to a man-in-the-middle attack. Further, it discusses a technique to prevent this attack in the digital signature. Â© 2020 IEEE.
Nonce: Life Cycle, Issues and Challenges in Cryptography
(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Sharma, S.; Jain, S.; Chandavarkar, B.R.
We all are living in the era of online processing, where the maximum of the information is available online. As the facilities of computer technology have increased, threats of losing personal and sensitive information have also increased. Cryptographic software and algorithms are good at some extent but as we all are seeing several attacks like Plaintext attack, Replay attack on Apply pay, Interleaving attack on PKMv2, etc. show us that our cryptographic software is less likely to be broken due to the weakness in the underlying deterministic cryptographic algorithms. A nonce is another attempt to improve security from these kinds of attacks. A nonce is an input value that will not repeat in a given context. Nonce use to prevent replay and interleaving attacks. Nonce also protects websites against malicious exploits that are based on Cross-Site Request Forgery (CSRF). The main aim of this paper is to introduce, What is Nonce, how it works and what are the issues and challenges in cryptography that we can solve with Nonce. Â© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.