Advancements in Deep Learning for Genomic Data Analysis

Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in autonomous driving. Sensor fusion techniques enhance perception capabilities, such as lidar, radar, and camera data integration. Additionally, advancements in semantic segmentation and object detection algorithms improve scene understanding and obstacle recognition. Collaborative efforts between academia, industry, and government agencies are essential for accelerating the deployment of autonomous driving technology in Indian road conditions. In conclusion, leveraging cutting-edge deep learning methodologies enables safe and efficient navigation of autonomous vehicles, ushering in a transformative future in transportation systems.

Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

- In recent months, the rise of free, deep learning- based software tools has significantly simplified the creation of highly realistic face-swapped videos, commonly known as "Deep Fakes" (DFs). While video manipulation through visual effects has been practiced for decades, recent advancements in deep learning have drastically enhanced the realism and accessibility of such synthetic content. Creating DFs using AI tools has become relatively straightforward; however, detecting these manipulations remains a major challenge. This is due to the complexity involved in training algorithms to recognize subtle and often imperceptible signs of tampering. In this work, we present a deep learning-based approach that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to detect Deep Fake videos. Our system employs CNNs to extract spatial features at the frame level, which are then passed to an RNN that captures temporal inconsistencies between frames introduced during Deep Fake generation. We evaluate our method on a large dataset of manipulated videos and demonstrate that our approach achieves competitive results using a relatively simple architecture. Key Words: Deep fake video detection, Convolutional neural networks(CNN), Recurrent neural networks(RNN)

Emotion detection, hence, is an important step toward making human-computer interaction a more enhanced process, where systems are made capable of identifying and responding to the emotional state of users. In fact, multimodal emotion detection systems in which both auditory and visual information are fused are emerging, and these approaches toward expressive emotional states are complementary and robust. Multimodal systems enhance the quality of interacting and, through many applications, can diagnose emotional disorders, monitor automotive safety, and improve human-robot interactions. In nature, the high-dimensional space and dynamic threats have resulted in obtaining low accuracy and high computational cost in applying the traditional models based on single-modality data. On the other hand, multimodal systems explore the synergy between audio and visual data, giving better performance and higher accuracy in inferring subtle emotional expressions. The latest improvement was done on these systems using recent advancements in transfer learning and deep learning techniques.That being said, this research Proposal devises a multimodal emotion recognition system integrating speech and face information through transfer learning for improved accuracy and robustness. Serving this purpose, the objectives of this research entail the effective comparison among different transfer-learning strategies, including the impact of pre-trained models in speech-based emotion recognition, and to introduce the role of voice activity detection in the process. Advanced neural network architectures like Spatial Transformer Networks and bidirectional LSTM in facial emotion recognition will also be tested. Early and late fusion strategies will also be used to find the best strategy for combining speech and facial data.This research will target several challenges that involve the complexity of data, balancing of the model performance-robustness balance, computational limitations, and standardization of evaluations in developing a working and robust emotion recognition system to enhance digital interaction and apply in practical areas. The goal is to create a system that oversteps the limitation of single-modality models through state-of-the-art advances in deep learning, as well as front-line improvements in transfer learning, in the manner of emotion detection performance.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Following recent advancements in deep learning and artificial intelligence, spoken language identification applications are playing an increasingly significant role in our day-to-day lives, especially in the domain of multi-lingual speech recognition. In this article, we propose a spoken language identification system that depends on the sequence of feature vectors. The proposed system uses a hybrid Convolutional Recurrent Neural Network (CRNN), which combines a Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN) network, for spoken language identification on seven languages, including Arabic, chosen from subsets of the Mozilla Common Voice (MCV) corpus. The proposed system exploits the advantages of both CNN and RNN architectures to construct the CRNN architecture. At the feature extraction stage, it compares the Gammatone Cepstral Coefficient (GTCC) feature and Mel Frequency Cepstral Coefficient (MFCC) feature, as well as a combination of both. Finally, the speech signals were represented as frames and used as the input for the CRNN architecture. After conducting experiments, the results of the proposed system indicate higher performance with combined GTCC and MFCC features compared to GTCC or MFCC features used individually. The average accuracy of the proposed system was 92.81% in the best experiment for spoken language identification. Furthermore, the system can learn language-specific patterns in various filter size representations of speech files.

Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia

Deep Learning is a rapidly evolving field with critical contributions to various domains including security, healthcare, and human — computer interaction, etc. It reviews the significant developments in the area of facial recognition using deep learning techniques. It explains deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs), as well as hybrid models and transfer learning uses. It also addresses technical, ethical, and legal challenges that arise for facial analysis systems and emphasizes the need for real-time processing, multi-modal systems, and robust algorithms to improve the technical accuracy and fairness of facial analysis.

Recent advances in deep learning have made a progressive impact on BCI researches. In particular, convolutional neural networks (CNNs) with different architectural forms have been studied for spatio-temporal or spatio-spectral feature representation learning. However, there still remain many challenges and limitations due to the necessity of a large annotated training samples for robustness. In this paper, we propose a semi-supervised deep adversarial learning framework that effectively utilizes generated artificial samples along with labeled and unlabelled real samples in discovering class-discriminative features to boost robustness of a classifier, thus to enhance BCI performance. It is also noteworthy that the proposed framework allows to exploit unlabelled real samples to better uncover the underlying patterns inherent in a user’s EEG signals. In order to justify the validity of the proposed framework, we conducted exhaustive experiments with ‘Recurrent Spatio-Temporal Neural Network’ CNN architectures over the public BCI Competition IV-IIa dataset. From our experiments, we could observe statistically significant improvements on performance, compared to the competing methods with the conventional framework. We have also visualized learned convolutional weights in terms of activation pattern maps, separability of extracted features, and validity of generated artificial samples.

Chest X-ray imaging plays a vital role in the evaluation of thoracic organs, serving as a primary diagnostic tool for various pulmonary conditions. Interpretations of chest X-ray reports can sometimes differ from a physician’s clinical judgment, leading to diagnostic inconsistencies and potential delays in treatment. This study explores whether the application of advanced deep transfer learning techniques can improve the accuracy of chest X-ray interpretation. Deep Learning presents powerful capabilities for processing and interpreting complex imaging data. Convolutional Neural Networks (CNNs) are widely used in deep learning to perform image classification through hierarchical feature extraction. training deep models with large annotated datasets often demands significant computational resources. In this research, MobileNet and Inception V3 architectures are employed to detect diseases such as lung cancer, pneumonia, and tuberculosis from chest X-ray images. Conducted a detailed evaluation of these models and compare their performance with traditional diagnostic approaches. The results demonstrate notable improvements in accuracy and sensitivity, confirming that deep transfer learning techniques significantly enhance diagnostic outcomes. This suggests that MobileNet and Inception V3 can serve as reliable, efficient tools to support radiologists in early detection and decision-making, and contributing to more timely and accurate medical diagnoses.

Hand gestures play a vital role in sign language, serving as a primary means of non-verbal communication for individuals who are deaf or mute, enabling them to express themselves and interact with others. Although numerous sign language recognition systems have been developed, most are either costly or lack adaptability for real-world use. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have significantly improved gesture recognition by enabling systems to learn complex patterns from large datasets of images or video sequences. This paper introduces a novel algorithm called Sign Language Accessibility Enhancement Using Ensemble of Deep Learning Techniques for Hand Gesture Recognition in Deaf and Dumb Communication (SLAEDL-HGRDDC). The model combines multiple components into a robust framework to enhance recognition accuracy and system scalability. Initially, Gaussian Filtering (GF) is used to reduce noise and improve image quality. Then, Inception-ResNet-V2 is adopted to extract intricate spatial features from gesture images. The recognition process utilizes an ensemble of three deep learning classifiers – Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Temporal Convolutional Networks (TCN). Finally, the Modified Al-Biruni Earth Radius Optimization (MBER) algorithm is employed for optimal hyperparameter tuning. Extensive simulations demonstrate that SLAEDL-HGRDDC outperforms traditional systems in various performance metrics.

Radar-based target recognition plays a crucial role in a variety of applications, such as surveillance, defense, and autonomous systems. High-resolution radar imagery, when processed effectively, can provide detailed information about objects of interest. However, due to the complex nature of radar signals and the limitations of traditional processing methods, extracting accurate and reliable target information remains challenging. Recent advancements in deep learning, particularly in the domain of image and video processing, have opened new avenues for improving radar-based target recognition. The primary challenge in radar target recognition is the effective use of high-resolution radar imagery, which often contains noise, motion blur, and other distortions. Traditional signal processing techniques struggle to handle these complexities, leading to reduced accuracy in real-world applications. Further, most existing methods are not well-equipped to handle the temporal dynamics and motion information inherent in radar-based video data, which is vital for identifying and tracking moving targets. This paper proposes a novel deep video processing technique designed for radar-based target recognition using high-resolution images. The approach leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract spatial and temporal features from radar video sequences. By integrating image enhancement algorithms and advanced feature fusion techniques, the system is capable of processing high-resolution radar frames in real-time. The method involves a two-stage process: first, extracting high-level spatial features from individual radar images using CNNs; second, capturing temporal relationships between frames with RNNs for robust target identification and tracking. Experimental results on a radar video dataset show significant improvements in target recognition accuracy. The proposed technique achieves a recognition rate of 94.3% in identifying static and dynamic targets, outperforming traditional methods by 15-20%. In terms of processing speed, the method demonstrates real-time performance with an average frame processing time of 32 ms, ensuring its suitability for operational environments. The system also demonstrates robustness against noise, with a decrease in false positive rates by 12%.

The integration of IDS and Internet of Things (IoT) with deep learning plays a significant role in safety. Security has a strong role to play. Application of the IoT network decreases the time complexity and resources. In the traditional intrusion detection systems (IDS), this research work implements the cutting-edge methodologies in the IoT environment. This research is based on analysis, conception, testing and execution. Detection of intrusions can be performed by using the advanced deep learning system and multiagent. The NSL-KDD dataset is used to test the IoT system. The IoT system is used to test the IoT system. In order to detect attacks from intruders of transport layer, efficiency result rely on advanced deep learning idea. In order to increase the system performance, multi -agent algorithms could be employed to train communications agencies and to optimize the feedback training process. Advanced deep learning techniques such as CNN will be researched to boost system performance. The testing part an IoT includes data simulator which will be used to generate in continuous of research work finding with deep learning algorithms of suitable IDS in IoT network environment of current scenario without time complexity.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

The aging global population is experiencing a rising prevalence of age-related hearing loss. Conventional hearing aids often fail in noisy environments, leading to user dissatisfaction. Recent advancements in deep learning, particularly in automatic speech recognition (ASR) and smart filter, highlight the potential for a new generation of hearing aids. This paper explores the transformative potential of evolving deep learning architectures to address the critical limitation of noise suppression. We review the progression of models specifically for auditory processing: from Deep Neural Networks (DNNs) for basic noise reduction, to Convolutional Neural Networks (CNNs) which analyse spectral features in audio spectrograms. Recurrent Neural Networks (RNNs) and sequence-to-sequence (seq2seq) models and transformer models which is a improved version of the Seq2Seq that further improved the handling of temporal speech patterns. We conclude that integrating these sophisticated models into next-generation hearing aids is essential for dramatically improving speech intelligibility in complex settings. This technological evolution promises to enhance the quality of life for the aging population by reducing hearing effort and promoting social engagement.