Drug Discovery and Medical Treatment
Deep learning plays a significant role in drug discovery and medical treatment as it is increasingly being used for automating the process of invention of new chemical entities and mining of large databases in health-privacy-protected vaults. The failure of allopathy to produce consistent results across varied sets of patients along with the genomics revolution has spurred the use of precision medicine. Precision medicines have enabled scientists and doctors to take certain decisions of treatment based on whether a drug will be effective, ineffective, or toxic, by identifying the genomics of patients and their diseases. Deep learning has widespread applications in the development of precision medicine. One of the major challenges in taking advantage of the advances in genomics is to decode the multi-faceted regulatory system that controls gene expressions. The regulation of gene expression involves many factors involving DNA methylation, regulatory RNAs, and transcription factors. Deep learning neural networks such as CNN and prediction network offer an ideal solution to meet these challenges in the field of drug discovery. So far, various deep learning models have been developed to precisely predict new molecular entities as plausible therapeutic scaffolds. Pharmaceutical giants such as Bayer Healthcare and Roche have contributed to computer-assisted drug design technologies for developing optimized pharmacophores. Increasing developments in deep learning models are expected to transform the fields of drug discovery and medical treatment.
Predictive Maintenance
Predictive maintenance is used for reducing the downtime and cost of maintenance under the premise of zero failure manufacturing by utilizing real-time data to forecast potential faults. Predictive maintenance depends heavily on the foundation of correlation techniques. Deep learning has widespread applications in predictive maintenance for mechanical and electrical systems in industries of manufacturing. The introduction of industry 4.0 has changed the manufacturing processes, maintenance management, and maintenance strategies significantly. Industry 4.0 has combined strengths of optimized manufacturing with internet technologies, cyber-physical systems (CPS), internet of things (IoT), and internet of services (iOS) which has led to the generation of large amounts of data, which can be fed to deep learning to develop new maintenance strategies. Deep learning has offered computation of hierarchical features or representation from objective data. Deep learning offers feature extraction without the need of human expertise through a generalized self-learning procedure which has made it ideal for use in predictive maintenance in the manufacturing industry. Currently, deep learning algorithms such as deep neural networks and deep belief networks have been successfully applied in predictive maintenance.
Classification algorithms such as SVM (Support Vector Machine), SOM (Self-organizing map), and BPNN (Back-Propagation Neural Networks) are being used for health diagnosis of complex systems such as aircraft systems and electric power transformers. Deep learning algorithms such as DNN (deep neural network), SAE (Sparse Autoencoder), DBN (Deep Belief Network), LSTM (Long Short-Term Memory), and CNN (Convolution neural network) offer superiority in certain processes such as degradation mapping, failure identification, fault characteristics mining, feature extraction about failures from raw input data, and knowledge discovery capabilities over conventional machine learning algorithms.
Customer Service Management and Personalized Service Offerings
Machine learning has been used for marketing automation for over a decade. Machine learning algorithms enable business owners to personalize customer experience based on their history of interactions, like purchasing habits, behavioral traits, and digital preferences. Deep learning technology, on the other hand, has further improved personalization processes by including customer intent to interaction history of the customers. The recommender systems used by businesses ranging from eCommerce stores to publishers and marketing agencies to drive sales, increase engagement,and improve overall user experience are increasingly using deep learning algorithms due to the benefits they offer over traditional machine learning algorithms. Personalized customer services such as chatbots rely on deep learning models to offer personalized responses to customers. Deep learning algorithms are also being used to drive customer retention. Deep learning caters to several factors, such as customer preference, personal preferences, spending patterns, and micro preferences and combines it with external factors, such as weather, to generate highly customized relevant suggestions which significantly increases customer retention. Moreover, deep learning algorithms such as CNN (Convolution neural network), RNN (recurrent neural network), and LSTM (Long Short-Term Memory) are being used for sentiment analysis on various social media websites which has further benefitted the development of marketing automation. When it comes to marketing automation in terms of personalization customized responses deep learning is considered as the future.
Logistics Optimization
Deep learning is currently transforming the logistics industry. Currently, the most prominent application area is predictive analytics. In the logistics industry, deep learning models enable shippers to optimize carrier selection, rating, routing, and quality control processes that save costs and improve efficiencies. Deep learning models optimize analytics solutions to consider dynamic attributes like weather or traffic and self-evolve over time to recognize patterns which optimizes the logistics operations significantly. The ability of deep learning models to utilize data across multiple systems and data sources like GPS systems, historical pricing performance, and FMCSA (Federal Motor Carrier Safety Administration) enables shippers to predict demand, analyze trends in supply chains, monitor seasonal calendars, and track daily patterns within lanes. Moreover, natural language processing, which is a part of machine learning, has optimized supply chains significantly by speeding up data entry and auto-populating form fields. Natural language processing is used for customizing customer responses by auto-populating shipping orders, bills of lading, and other transactions. Moreover, deep learning has applications in other areas of the supply chain such as predictive equipment maintenance, yield optimization, procurement analytics, and inventory optimization.
Voice and Image Recognition
Voice and image recognition are some of the major applications of deep learning. Deep learning is extensively used for image recognition, object detection, feature mapping for computer vision. Deep learning models for object detection have already surpassed human performance. CNN, a deep learning algorithm, is increasingly being used for purposes of object detection and feature extraction due to various advantages such as ruggedness to shifts and distortion in the image, fewer memory requirements, and easier and better training. Object detection is used across different fields such as robotics, automation in manufacturing, medical image detection, safety systems, and more. Voice recognition is another application with the widespread use of deep learning models. Automatic speech recognition is still a challenging task due to different factors such as different accents, dialects, or pronunciations, different styles, and different rates of speakers. Moreover, the presence of environmental noise, reverberation, different microphones, and recording devices results in additional variability which makes it a difficult task for conventional algorithms. Deep learning due to its unsupervised learning type is becoming a mainstream technology for speech recognition. Deep learning algorithms such as deep belief networks are being used for speech recognition and feature coding at an increasingly larger scale.
