Deep Learning and Machine Learning based Facial Emotion Detection using CNN

Singh, Shubham Kumar; Thakur, Revant Kumar; Kumar, Satish; Anand, Rohit

doi:10.23919/indiacom54597.2022.9763165

Cited by 130 publications

(126 citation statements)

References 17 publications

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“…However, the complexity of ML models makes it challenging to elucidate how they fit the data. This poses a major challenge in cases where the models fit to irrelevant patterns in the data, leading to the development of a poor-quality model that shows high performance (16). Treating such models as “black boxes” limits insight into the biological mechanism the model seeks to describe.…”

Section: Discussionmentioning

confidence: 99%

Asymmetric trichotomous data partitioning enables development of predictive machine learning models using limited siRNA efficacy datasets

Monopoli

Korkin

Khvorova

2022

Preprint

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Chemically modified small interfering RNAs (siRNAs) are promising therapeutics guiding sequence-specific silencing of disease genes. However, identifying chemically modified siRNA sequences that effectively silence target genes is a challenge. Such determinations necessitate computational algorithms. Machine Learning (ML) is a powerful predictive approach for tackling biological problems, but typically requires datasets significantly larger than most available siRNA datasets. Here, we describe a framework for applying ML to a small dataset (356 modified sequences) for siRNA efficacy prediction. To overcome noise and biological limitations in siRNA datasets, we apply a trichotomous (using two thresholds) partitioning approach, producing several combinations of classification threshold pairs. We then test the effects of different thresholds on random forest (RF) ML model performance using a novel evaluation metric accounting for class imbalances. We identify thresholds yielding a model with high predictive power outperforming a simple linear classification model generated from the same data. Using a novel method to extract model features, we observe target site base preferences consistent with current understanding of the siRNA-mediated silencing mechanism, with RF providing higher resolution than the linear model. This framework applies to any classification challenge involving small biological datasets, providing an opportunity to develop high-performing design algorithms for oligonucleotide therapies.

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Section: Discussionmentioning

confidence: 99%

Asymmetric trichotomous data partitioning enables development of predictive machine learning models using limited siRNA efficacy datasets

Monopoli

Korkin

Khvorova

2022

Preprint

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“…While there are many machine-learning methods that can be used for this type of classification problem [58], we restricted ourselves to xGBoost models and neural net models, creating one of each for each training set. The hyperparameters of the models were tuned by hand, and the model’s performance at this stage was determined on a withheld dataset composed of 3741 points for the dataset varying pressure, and 2201 points for the dataset varying the difference in bending rigidity of the MP patch from that of the rest of the membrane.…”

Section: Methodsmentioning

confidence: 99%

Modelling membrane curvature generation using mechanics and machine learning

Malingen

Rangamani

2022

J. R. Soc. Interface.

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The deformation of cellular membranes regulates trafficking processes, such as exocytosis and endocytosis. Classically, the Helfrich continuum model is used to characterize the forces and mechanical parameters that cells tune to accomplish membrane shape changes. While this classical model effectively captures curvature generation, one of the core challenges in using it to approximate a biological process is selecting a set of mechanical parameters (including bending modulus and membrane tension) from a large set of reasonable values. We used the Helfrich model to generate a large synthetic dataset from a random sampling of realistic mechanical parameters and used this dataset to train machine-learning models. These models produced promising results, accurately classifying model behaviour and predicting membrane shape from mechanical parameters. We also note emerging methods in machine learning that can leverage the physical insight of the Helfrich model to improve performance and draw greater insight into how cells control membrane shape change.

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“…In SL, the training set comprises the input data set and the desired output, referred to as labels. SL learns a function (a match between the input data and the result data) by extracting information from the input data and the labels fed into the machine [156]. SL problems are divided into two main categories, which are regression and classification [157].…”

Section: Supervised Learning (Sl)mentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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Deep Learning and Machine Learning based Facial Emotion Detection using CNN

Cited by 130 publications

References 17 publications

Asymmetric trichotomous data partitioning enables development of predictive machine learning models using limited siRNA efficacy datasets

Asymmetric trichotomous data partitioning enables development of predictive machine learning models using limited siRNA efficacy datasets

Modelling membrane curvature generation using mechanics and machine learning

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

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