A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Naskath, J.; Sivakamasundari, G.; Begum, A. Alif Siddiqua

doi:10.1007/s11277-022-10079-4

Cited by 56 publications

(29 citation statements)

References 60 publications

(68 reference statements)

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“…SSL shows promise when labeled data sets are scarce or costly. 2a) and layers, influencing information flow 118 Researchers optimize ANNs for specific tasks by adjusting parameters. FNNs handle classification and regression, RNNs process language and speech, and SOMs aid clustering and visualization in drug toxicity studies.…”

Section: (J)mentioning

confidence: 99%

“…ANN architectures organize neurons (Figure a) and layers, influencing information flow and processing. Examples include feed-forward networks (FNNs) with unidirectional flow, recurrent neural networks (RNNs) with bidirectional flow, and Kohonen Self-Organizing Neural Networks (SOMs) with lateral flow. ,− RNNs capture temporal dependencies, while SOMs compete to identify the best matching unit (BMU) . Researchers optimize ANNs for specific tasks by adjusting parameters.…”

Section: A Brief Account On Deep Learningmentioning

confidence: 99%

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

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Section: (J)mentioning

confidence: 99%

Section: A Brief Account On Deep Learningmentioning

confidence: 99%

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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“…Recently, both traditional machine learning and deep learning techniques have shown promise in the classification and identification of patterns in images (Khan et al, 2020;Naskath et al, 2023). These methods can automatically learn features from large data sets, eliminating the need for manual parameter tuning.…”

Section: Introductionmentioning

confidence: 99%

Robust image descriptor for machine learning based data reduction in serial crystallography

Rahmani,

Nawaz,

Pennicard

et al. 2024

J Appl Crystallogr

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Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

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“…Due to the unstable and unreliable wireless links, the existing multi-hop wireless routing algorithm 12,13 generates a lot of packet retransmissions when retransmission is enabled. Since wireless devices share the limited bandwidth, this affects the throughput of the network.…”

Section: Introductionmentioning

confidence: 99%

Regular routine aware routing in opportunistic mobile social networks

Syed Rabiya,

Ramalakshmi,

Naskath

2023

Trans Emerging Tel Tech

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Due to intermittent connectivity, routing protocols in opportunistic mobile social networks (OMSN) forward messages through opportunistic contacts. Previous opportunistic routing protocols focus on the past contact history of nodes to make a forwarding decision. It was found that predicting temporally evolving contacts and communities according to different times of the day would improve the accuracy of the prediction. In this paper, a routing paradigm called RRoutine is proposed that allows message holders to exchange multiple copies of the message based on the predicted number of communities. This distribution continues until the remaining copies of the message become one. Each message holder applies either inter or intra‐community forwarding and selects a set of potential forwarders based on the prediction of future encounters in the community and an expected remaining duration between two nodes (ERIMD). The estimation of ERIMD takes into account the sequence of daily activities, the time scale of current activities, and the return time to regular meetings of the nodes. Also, unlike traditional routing, where a single forwarder is selected, a set of potential candidates is selected and prioritized according to their contact prediction, with the most likely candidate receiving the highest priority. Simulation results obtained from synthetic and real mobility data show that the proposed paper improves the performance of the system in terms of message delivery rate, throughput, and latency.

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A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Cited by 56 publications

References 60 publications

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Robust image descriptor for machine learning based data reduction in serial crystallography

Regular routine aware routing in opportunistic mobile social networks

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