An Effective Deep Learning Model for Surface-Enhanced Raman Spectroscopy Detection Using Artificial Neural Network

The healthcare system is transforming as a result of new technology invention and implementation, which promotes therapeutic interventions and treatment outcomes for improved patient care. Numerous diverse parameters like gene variation, sociodemographics, lifestyle/environmental elements, should be measured to expedite precision/personalized medicine (PM). As a result, one of the most important difficulties in PM is bridging the translational gap in the clinical context by transforming big, multimodal data into decision support tools. Deep learning (DL) offers a novel technique to overcome these challenges, enabling the building or acquisition of accuracy, multimodal predictive models that could help the PM vision become a reality shortly. DL along with modern artificial intelligence (AI), in particular, provides tools in the form of algorithms that could pave the way for an individualized malignancy treatment in a more precise and customized manner by allowing early diagnosis and precise treatment options. Machine learning (ML) techniques applied to genomic datasets have a lot of potentials to deliver PM. DL has the power to accurately analyze tumor data and recommend the best possible treatment for cancer patients with a personalized approach. The capacity to anticipate how a patient will respond to a medication using DL techniques will aid in the reevaluation of treatment decisions, as well as the reduction of errors and disease-related financial burden. This chapter discusses the overall characteristics of DL and PM, as well as the importance of DL in precision oncology drug discovery and development, genomics, and biomarker datasets. It also outlines the potential of different technologies of DL in cancer diagnosis, detection, and prediction. It also highlights the applications of DL in translational oncology and its clinical benefits. Various challenges and limitations associated with DL techniques, such as like unique genomic patterns, small sample size, multidisciplinary expertise, and clinical trials, are also addressed.

Multidisciplinary single-cell optical cytometry.

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

Deep Learning (DL) is popular among the researchers and academicians due to its reliability and accuracy, especially in the field of engineering and medical sciences. In the field of medical imaging for the diagnosis of disease, DL techniques are very helpful for early detection. Most important features of DL techniques are that they are uncomplicated with lower complexity, which ultimately saves the time and money and tackle many tough tasks simultaneously. Artificial Intelligence (AI) and Deep Learning (DL) technologies have rapidly improved in recent years. These techniques played an important role in every field of application, especially in the medical field such as in image processing, image fusion, image segmentation, image retrieval, image analysis, computer aided diagnosis (CAD), image registration and, image-guided therapy and many more. The aim of writing this chapter is to describe the DL methods and, the future of biomedical imaging using DL in detail and discuss the issues and challenges.

Evolving trends in SERS-based techniques for food quality and safety: A review

Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines. Today we are witnessing the tangible successes of Deep Learning in our daily lives in various applications, including education, manufacturing, transportation, healthcare, military, and automotive, etc.<strong> </strong>Deep Learning is a subfield of Machine Learning that stems from Artificial Neural Networks, where a cascade of layers is employed to progressively extract higher-level features from the raw input and make predictive guesses about new data. This paper will discuss the effect of attribute extraction profoundly inherent in training<strong> </strong>approaches such as Convolutional Neural Networks (CNN). Furthermore, the paper aims to offer a study on Deep Learning techniques and attribute extraction methods that have appeared in the last few years. As the demand increases, considerable research in the attribute extraction assignment has become even more instrumental. Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.

Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN) model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

Agriculture has been a vital sector for the majority of people, especially in countries like India. However, the increasing need for food production has led to intensive farming practices that have resulted in the deterioration of soil quality. This deterioration in soil quality poses significant challenges to both agricultural productivity and environmental sustainability. To address these challenges, advanced soil nutrient prediction systems that utilize machine learning and deep learning techniques are being developed. These advanced soil nutrient prediction systems utilize various sources of data, such as soil parameters, plant diseases, pests, fertilizer usage, and changes in weather patterns. By mapping and analyzing these data sources, machine learning algorithms can accurately predict the distribution of soil nutrients and other properties essential for precise agricultural practices. A previous study compared machine learning algorithms like SVM and Random Forest with deep learning algorithms CNN and LSTM for predicting crop yields. The most appropriate model is a significant challenge, but several studies have evaluated recommendation system models using deep machine learning techniques. Deep learning models attain accuracy above 90%, while many ML models achieve rates between 90% and 93%. Furthermore, the research seeks to propose specific crop suggestions grounded in soil nutrients for precision agriculture to enhance crop productivity.

A novel SERRS approach for the highly sensitive detection of bilirubin levels in urine.

Modern, highly developed technology has impacted reputable procedures in the medical and healthcare industries. Smart healthcare prediction to the senior sick patient is not only for quick access to data but also to get dependable treatment in an accurate prediction by healthcare service provider. smart health prediction helps in the identification of numerous diseases. Based on patient experience, Deep learning technology provides a robust application space in the medical sector for health disease prediction problems by applying deep learning techniques to analyze various symptoms. In order to classify things and make precise predictions about diseases, deep learning techniques are utilized. people's health will be more secure, medical care will be of a higher caliber, and personal information will be kept more secret. As deep learning algorithms become more widely used to construct an interactive smart healthcare prediction and evaluation model on the basis of the deep learning model, CNN is upgraded. Advanced deep learning algorithms combined with multi-mode approaches and resting-state functional magnetic resonance represent an innovative approach that researchers have taken. A DL structure for the programmed ID ASD using highlights separated from the corpus callosum and cerebrum volume from the Stand dataset is proposed. Imaging is used to reveal hidden diseased brain connectome patterns to find diagnostic and prognostic indicators.

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Recently, deep neural networks (DNNs) have been widely studied and achieved tremendous success in a variety of real-world applications, such as computer vision, natural language processing, medical diagnosis, and machine translation. Deep reinforcement learning (DRL), as an emerging powerful deep learning technique, combines DNNs with reinforcement learning into an interactive system that enables the agent to learn the best policy from the environment to optimize their rewards. DRL opens up many new applications in domains such as healthcare, robotics, and smart grids. With the rapid evolution of IT infrastructures, cloud computing has been witnessed as the most common computing paradigm. The underlying infrastructure of cloud computing relies on a large amount of data centers. Nowadays, with the fast development of communication technologies, such as 5G, cloud computing is now evolving into yet another new paradigm called edge computing, providing for emerging applications, especially in the Internet-of-Things. The energy efficiency issue from both "cloud" and "edge" are becoming more crucial and calls for more attention. To solve the real-world energy efficiency problems, we take advantage of the deep learning and deep reinforcement learning techniques for efficient modeling of both "edge" and "cloud" applications. On the "edge" side, wearable technologies and the Internet of Things (IoT) are becoming increasingly popular in both personal and commercial applications with the employment of the embedded system technologies. Non-Volatile Processors (NVPs) have been proposed for the intermittent power supply problem, to ensure the instant on/off for embedded systems under the unstable power supply. We proposed neural network-based prediction techniques to derive the best power extraction policy and converter parameters optimization technique for NVPs. In cloud computing, we formulated a novel optimal power management problem of data centers to minimize the total cost by proposing the long short term memory (LSTM) neural network-based method for the prediction of future data center power consumption, and further we presented a novel DRL-based hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. Furthermore, we explored the effectiveness of the DRL-based framework for high dimensional action and state spaces problem-dynamic treatment regimes, to model real-life complexity in heterogeneous disease progression and treatment choices, to provide doctors and patients the data-driven personalized decision recommendations. Last but not least, we explored the efficiency of deep neural networks. Structured weight pruning is a representative model compression technique of DNNs to reduce both the storage and computation requirements and meanwhile accelerate inference. An automatic hyperparameter determination process is necessary due to a large number of flexible hyperparameters. We propose an automatic structured pruning framework to compress the DNNs. Our framework outperforms the prior works on automatic model compression by up to 33× in pruning rate (120× reduction in the actual parameter count) under the same accuracy. Significant inference speedup has been observed from the proposed framework on actual measurements on the smartphone.

<p indent="0mm">With the rapid development of artificial intelligence technology, deep learning techniques have begun to be applied in geological researchs. Compared to traditional lithofacies identification methods, the application of deep learning not only resulted in the implementation of efficient automatic lithofacies identification but also resulted in the accuracy improvement and uncertainty reduction of interpretation. Although excellent achievements in the field of single-well log identification have been obtained by using the powerful learning capability of deep learning, it is found that the feature representation of a single modality can hardly encompass the complete information of relevant geological bodies. In order to overcome the limitations of feature representation in a single modality and to optimally utilize the values contained in multiple modalities, some scholars have proposed the application of multimodal fusion method to enhance the model learning performance. Based on the previous research status, in this paper, aiming to elaborate on the application and development prospects of deep learning-related technologies in the field of lithofacies identification, we have introduced representative traditional identification techniques and those techniques integrated with the machine learning and have further highlighted the necessity and huge potential of machine learning applied in the lithofacies identification. Additionally, we proposed a fusion strategy for the combination of multimodal fusion technology with deep learning in order to provide prior knowledge and methods for related researchers. Finally, we have briefly summarized the current fusion strategies and have elaborated the existing challenges in relevant geological researches.

The medical diagnosis of diseases involves the detection and classification of patients from medical image data. It has an important Artificial Intelligence (AI) application in Healthcare and Neuroscience fields. In addition, Machine Learning (ML) and Deep Learning (DL) techniques provide important tasks on Neurodegenerative Diseases (ND) diagnosis from Brain MRI (Magnetic Resonance Imaging) data for image processing with good classification results. This chapter aims at comprehensive and comparative study on the performance of medical image classification with those techniques using Brain MRI datasets. We introduce a brief overview of the medical diagnosis of ND with AI applications in the neuroscience domain. In the literature part, we present the understanding of the medical image classification with Machine and Deep Learning techniques. Therefore, we describe the concept of MRI data and the Brain MRI datasets that are used in this work. In the comparison part, we focus on related works of the Artificial Neural Network (ANN) and the Deep Convolutional Neural Network (DCNN) models and we compare these models in the literature. In the methodology part, we combine the ANN and DCNN in order to propose a new Deep model named ANN-DCNN model when it is more accurate and effective to classify from those Brain images. To improve the performance of those Neural Networks in medical diagnosis applications, we discuss the recent insights and future directions.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.