Chapter 3 - Sensor-enabled biomedical decision support system using deep learning and fuzzy logic

The advent of the fourth industrial revolution (industry 4.0) has sparked a transformation in manufacturing through the integration of deep learning (DL) and big data technologies. These innovations have enhanced the ability of manufacturers to process and analyze vast amount of data, providing insights that improve decision-making, efficiency, and overall productivity. Deep learning, a subset of artificial intelligence (AI), offers advanced capabilities in data pattern recognition and predictive modeling, while big data facilitates the management of large and complex datasets from various sources. However, literature on the integration of deep learning, big data and industry 4.0 is still limited in the manufacturing context. This paper provides a detailed overview of big data, deep learning and industry 4.0 in manufacturing. It explores the applications of deep learning and big data in manufacturing, highlighting their role in optimizing production processes, predictive maintenance, quality control, and supply chain management. Furthermore, the paper addresses the key challenges and solutions associated with the integration of these technologies, such as data privacy, security, computational complexity, and the need for skilled labor. KEYWORDS: Deep learning, Industry 4.0, Supply chain management, Big data, Predictive maintenance, Artificial intelligence, ERP systems.

A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques

With the rapid development of intelligent transportation, autonomous driving has become a core direction of technological innovation. Its safe and efficient operation relies on precise processing of multi-source sensor signals. Traditional signal processing methods, limited by manual feature design, often fail to extract complete features and suppress noise in complex traffic environments. This leads to perception biases and decision-making delays, posing safety hazards and restricting the implementation of autonomous driving technology. By combining literature review and technical analysis, this paper focuses on the integration of deep learning and autonomous driving signal processing, aiming to explore how to enhance the efficiency and reliability of signal processing through deep learning algorithms. It concludes that deep learning, through its adaptive feature learning ability, can significantly improve the real-time performance and accuracy of signal processing. However, challenges such as data scarcity, poor model interpretability, and high computational costs still exist. Future research should focus on technological breakthroughs such as lightweight model design and multi-sensor fusion to promote the large-scale application of deep learning in autonomous driving signal processing.

Quantum mechanics with deep learning integration has the ability to radically alter current approaches to predicting rice yields, according to recent innovations in agricultural technology. By combining the complex processing power of quantum computing with the strong pattern recognition capabilities of deep-learning(DL) strategies like Bidirectional Long-Short-Term-Memory(Bi-LSTM) and Extreme-Gradient-Boosting(XGBoost), this research presents a new model of hybrid quantum deep learning. To process quantum features, one uses quantum circuits, whereas to extract temporal features, one uses bi-LSTM networks. To improve data representation, quantum circuits capture complex feature interactions by leveraging quantum entanglement and superposition. A combination of the enhanced quantum features and the time-based features retrieved by Bi-LSTM are then inputted into an XGBoost regression model. We hope to achieve a substantial improvement in prediction accuracy with our approach, which combines conventional machine learning with quantum feature processing. The results are 0.00011 for mean-square-error(MSE), 0.99992 for coefficient-of-determination(R2), and 0.00139 for mean-average-error(MAE), according to the aforementioned measurements. Crucial decisions in worldwide agronomic planning and organization are aided by this value, which is so near to perfection.

An integration of deep learning and classical filtering techniques has been developed for achieving an accurate and robust positioning system. Despite significant technological advances, positioning systems often face fundamental challenges such as signal obstruction, multipath interference, and error drift. Although 3D map matching techniques using LiDAR sensors can mitigate these challenges, their practical implementation is limited by high costs, computational burdens, and dependency on pre-modeled environments. In response, recent research has increasingly focused on enhancing positioning systems through deep learning–based sensor fusion and adaptive filtering methods, emphasizing improvements in accuracy and operational robustness. In this paper, a seamless hierarchical context-aware intelligent positioning system (SH-CAIPS) is proposed by integrating sensor fusion, two-stage outlier mitigation gates, and deep reinforcement learning (DRL)-based noise scaling with an innovation-based adaptive Kalman filter (IAKF). As a result, the proposed positioning system can enhance robustness by adaptively adjusting measurement noise and dynamically updating the position based on the confidence score between model predictions and sensor measurements. From simulation results, it is confirmed that the positioning performances can be significantly improved based on the proposed SH-CAIPS compared with conventional positioning systems.

In the domain of image processing, deep learning has assumed the position of the spearhead of innovation, creating revolutionary changes in numerous fields, such as image categorization, object recognition, and image generation. This white paper aims at developing a more detailed overview of the modern deep learning methods which are primarily designed for image processing. Analyzing the essential aspects of CNNs, GANs, and RNNs we wish to reveal innovative achievements in this area. In this white paper, it highlights a broad variety of domains, where these deep learning techniques have demonstrated remarkable progress. In the field of medical imaging, where CNNs help in the diagnosis and prognosis of the disease, to satellite imagery analysis, where GANs improve the generation of synthetic realistic data, and computer vision, where RNNs allow for better video analysis, deep learning is redefining the limits of image processing. Development should also be highlighted as an issue that the integration of deep learning into the image processing pipeline may bring along with it, including data accessibility, model interpretability, and computing resources. Accordingly, acknowledging such issues and staying updated with new trends, researchers and practitioners will be able to fully utilize deep learning and use it to solve complex image processing problems and carry out innovation in many different spheres of life. Deep learning is here to stay at the cutting edge of image processing, transforming our abilities and perspectives in this ever - changing discipline.

An integration of deep learning and symbolic logic is proposed, based on the Curry-Howard isomorphism and categorical logic. The propositional structure of logic is seen as a symmetry, namely the permutation invariance of propositions; This can be implemented using so-called symmetric neural networks. Under our interpretation, it turns out that Google’s BERT, which many currently state-of-the-art language models are derived from, can be regarded as an alternative form of logic. This BERT-like structure can be incorporated under a reinforcement-learning framework to form a minimal AGI architecture. We also mention some insights gleaned from category and topos theory that point to future directions and may be helpful to other researchers, including mathematicians interested in AGI.KeywordsDeep learningSymbolic logicLogic-based AINeural-symbolic integrationCurry-Howard isomorphismCategory theoryTopos theoryFuzzy logic

Introduction to the special issue on deep learning approaches for machine translation

Due to development in cloud services, lots of data is transferred between users and servers of the cloud. This transmission of data consumes huge amounts of energy. This energy consumption occurs during the operation of network infrastructure, the conversion of electrical to optical signals to travel long distances, and signal amplification. As Green computing is the use of computing devices in an environmentally friendly way, i.e., using electrical energy efficiently as Data centers require a significant amount of electricity to operate and cool the servers, leading to carbon emissions from the burning of fossil fuels. Green computing in cloud services is about optimizing energy consumption and by incorporating deep learning algorithms, we can enhance the energy efficiency of cloud infrastructure. These algorithms can analyze real-time data from sensors, optimize resource allocation, and dynamically adjust power usage. Through intelligent workload scheduling, server consolidation, and power management, deep learning enables the reduction of energy waste and carbon emissions. The integration of deep learning in cloud services not only improves energy efficiency but also enhances performance and cost-effectiveness. Here we are using a deep learning model which can be used for workload prediction and resource provisioning. By analyzing historical workload patterns and user behavior, deep learning algorithmscan predict future resource demands and allocate resources accordingly, leading to more efficient resource utilization and energy savings

Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of “big data” and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.

Stroke is considered a global disease that leads to death and certain neural disabilities. Deep learning techniques have revolutionized EEG-based Brain-Computer Interface(BCI), enhancing reliability and promising a transformative future for BCI applications in stroke rehabilitation. However, BCI's implementation in clinical practice has been restricted due to their low accuracy performance. The objective of this review is to summarize how the integration of deep learning and BCI technologies can contribute to the rehabilitation of stroke patients. This paper compiles studies that evaluated deep learning and BCI intersection for stroke subjects, analyses the methodological quality of these studies, and verifies the relationship between the effects of the interventions and performance achieved in rehabilitation. The deficiencies and the future development direction of stroke rehabilitation in BCI with deep learning are also discussed. The various deep learning techniques combined with BCI technology, will improve people's ability to cope with stroke and provide a way to recover from stroke.

With university English blended teaching mode combined with deep learning marks a significant advancement in language education. With integrating deep learning techniques into blended teaching approaches, educators can leverage vast amounts of educational data to enhance the effectiveness and personalization of English language instruction. Deep learning algorithms analyze student interactions, performance data, and learning preferences to tailor instructional content and activities to individual needs. This model enables educators to create dynamic and adaptive learning experiences that blend traditional classroom instruction with online resources and digital tools. The paper presents a comprehensive examination of blended teaching methodologies within the realm of English education, integrating principles of deep learning to enhance learning outcomes. Through a series of experiments and analyses, the study investigates the effectiveness of various teaching components, the utilization of online resources, and the application of diverse deep learning architectures. Findings underscore the significance of a balanced integration of traditional pedagogies and digital resources, with online materials emerging as a pivotal aspect of the teaching strategy. The study conducted experiments evaluating the effectiveness of blended teaching methodologies in English education, with numerical scores ranging from 4.2 to 9.0 across different parameters. Results indicated that experiments with higher scores for traditional teaching effectiveness (ranging from 6.5 to 9.2) and deep learning integration (ranging from 3.9 to 9.1) tended to yield superior overall learning outcomes (ranging from 4.3 to 9.0). Furthermore, online resources received consistently high weighted scores (ranging from 7.2 to 9.5), underscoring their significant contribution to the overall teaching strategy.

The integration of deep learning and blockchain technologies has the potential to revolutionize soil quality prediction in smart agriculture. Deep learning models, like neural networks and convolutional neural networks, enable accurate predictions of soil properties by considering intricate relationships within data. Contextual learning approaches, including embeddings and data fusion, enrich the prediction process by incorporating external factors like weather conditions and land management practices. Blockchain technology ensures secure storage of predictions and data, while smart contracts facilitate automated model execution. This integrated system empowers farmers with accurate predictions for optimal resource allocation and fosters collaboration through decentralized data sharing. Future directions include advancements in deep learning algorithms, blockchain applications, and potential integration with IoT and remote sensing technologies.

Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.

The proposed system's efficacy is evaluated through extensive simulations and real-world testing, demonstrating high accuracy in accident detection and timely activation of the emergency response. This integration of deep learning with OpenCV and an automated alert system aims to reduce response times and enhance the chances of survival and support in the critical moments following a car accident. Keywords--- Deep Learning, OpenCV, Emergency Call, Message Notification, Email Alert