A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment

Shrivastava, Rahul; Kumar, Parvinder; Tripathi, Sudhakar; Tiwari, Vivek; Rajput, Dharmendra Singh; Gadekallu, Thippa Reddy; Suthar, Bhivraj; Singh, Saurabh

doi:10.3390/app10155147

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…The Ensemble learning approach, which uses the three most popular machine learning algorithms and implements a majority voting scheme, gave comparable results for all languages. This finding can be very helpful for developing an emotion recognition system for robots designed to handle customers from all corners of the globe [39]. It will enable the robots to interact with customers smartly with emotional intelligence, which can have a huge impact on the way the world interacts with robots.…”

Section: Resultsmentioning

confidence: 95%

Cross corpus multi-lingual speech emotion recognition using ensemble learning

Zehra

Javed

Jalil

et al. 2021

Complex Intell. Syst.

Self Cite

103

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Receiving an accurate emotional response from robots has been a challenging task for researchers for the past few years. With the advancements in technology, robots like service robots interact with users of different cultural and lingual backgrounds. The traditional approach towards speech emotion recognition cannot be utilized to enable the robot and give an efficient and emotional response. The conventional approach towards speech emotion recognition uses the same corpus for both training and testing of classifiers to detect accurate emotions, but this approach cannot be generalized for multi-lingual environments, which is a requirement for robots used by people all across the globe. In this paper, a series of experiments are conducted to highlight an ensemble learning effect using a majority voting technique for cross-corpus, multi-lingual speech emotion recognition system. A comparison of the performance of an ensemble learning approach against traditional machine learning algorithms is performed. This study tests a classifier’s performance trained on one corpus with data from another corpus to evaluate its efficiency for multi-lingual emotion detection. According to experimental analysis, different classifiers give the highest accuracy for different corpora. Using an ensemble learning approach gives the benefit of combining all classifiers’ effect instead of choosing one classifier and compromising certain language corpus’s accuracy. Experiments show an increased accuracy of 13% for Urdu corpus, 8% for German corpus, 11% for Italian corpus, and 5% for English corpus from with-in corpus testing. For cross-corpus experiments, an improvement of 2% when training on Urdu data and testing on German data and 15% when training on Urdu data and testing on Italian data is achieved. An increase of 7% in accuracy is obtained when testing on Urdu data and training on German data, 3% when testing on Urdu data and training on Italian data, and 5% when testing on Urdu data and training on English data. Experiments prove that the ensemble learning approach gives promising results against other state-of-the-art techniques.

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Section: Resultsmentioning

confidence: 95%

Cross corpus multi-lingual speech emotion recognition using ensemble learning

Zehra

Javed

Jalil

et al. 2021

Complex Intell. Syst.

Self Cite

103

View full text Add to dashboard Cite

show abstract

“…However, like any other technology, this also comes with many drawbacks, mainly privacy and security dilemmas. In ( 13 ), the authors have presented a grid and place neuron model in cognitive tasks applications. The 2D virtual environment was created to deploy this system, which resulted in 92.27 % localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems

Akhtar

Nazir

Saleem

et al. 2022

Front. Public Health

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In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework.

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“…The performance comparison was accomplished based on the evaluation metrics-precision score, recall/sensitivity score, F1 score, and specificity score [34]. True positive, true negative, false positive, and false negative values were used to compute the evaluation metrics [35][36][37][38][39][40][41]. The results were plotted using Python's Matplotlib library for better interpretation and visualization.…”

Section: Execution Of Vgg16 Modelmentioning

confidence: 99%

Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

Srinivasan¹,

Garg²,

Datta³

et al. 2021

Computers, Materials &Amp; Continua

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According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semiautomated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver's distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research develops a deep convolutional neural network (deep CNN) models for predicting the reason behind the driver's distraction. The deep CNN models are trained using numerous images of distracted drivers. The performance of deep CNN models, namely the VGG16, ResNet, and Xception network, is assessed based on the evaluation metrics, such as the precision score, the recall/sensitivity score, the F1 score, and the specificity score. The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers' activities.

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A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment

Cited by 12 publications

References 40 publications

Cross corpus multi-lingual speech emotion recognition using ensemble learning

Cross corpus multi-lingual speech emotion recognition using ensemble learning

A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems

Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

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