Search

This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]

These papers collectively showcase the adaptability and effectiveness of data mining techniques, making substantial contributions to the broader realm of " I Methods and Applications of Data Mining in Business Domains i ". In a business context, the challenge is that one would like to see (i) how the algorithms can be repeatable in the real world, (ii) how the patterns mined can be utilized by the business, and (iii) how the resulting model can be understood and utilized in the business environment [[1]]. Additionally, they provide insights into factors influencing the adoption of business intelligence systems (BISs) in small and medium-sized enterprises (SMEs) [[26]], and conduct a systematic literature review on AI-based methods for automating business processes and decision support [[27]]. This Special Issue invited researchers to contribute original research in the field of data mining, particularly in its application to diverse domains, like healthcare, software development, logistics, and human resources. [Extracted from the article]

The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue on the application of machine learning in geotechnical engineering. The issue includes 19 articles that explore different applications of machine learning in this field, such as determining geotechnical parameters, predicting geotechnical disasters, and optimizing construction processes. The articles cover various topics in geotechnical engineering, including underground and foundation engineering, and discuss the use of machine learning algorithms to predict and estimate various parameters and behaviors. While machine learning shows potential in improving predictions, the papers also acknowledge the limitations of purely data-driven models and the need to incorporate mechanical models and improve data collection methods. Overall, these articles provide valuable insights and serve as a starting point for future research in the field. [Extracted from the article]

A Multilevel Inverter (MLI) meets the power requirements of most power electronic applications. It can provide an AC supply with better power quality. The higher number of levels in an MLI results in better power quality output. The fundamental question that comes to mind is what the optimal number of levels can be in designing an MLI for better performance. This paper aims to provide a comparison of three-, five-, and seven-level schemes based on a "Diode Clamped MLI" and will analyze their performance on the reduction of Total Harmonic Distortion (THD). [ABSTRACT FROM AUTHOR]

A dual-band UWB coplanar waveguide monopole antenna is presented in this paper. The antenna proposed in this paper consists of an FR4 substrate, two ground planes, and a rectangular patch. The size of the antenna proposed is 24 x 25 x 1 mm³ with a slit of length and width of 5.7 and 0.5 mm, respectively. We received two operating bands, a C- and Ku-band, with both Bi and Omni-directional radiation patterns. The bandwidth values of the first (3.65-7.49) GHz and second (11.5-13.4) GHz band of the proposed antenna are 3.9 GHz and 1.7 GHz, respectively. The proposed antenna operating at a frequency 5.8 GHz has a maximum bandwidth of 4.5 GHz and minimum bandwidth of 1.6 GHz. The characteristics of this antenna show that the antenna proposed in this paper is suitable for dual-band and UWB communication systems. We achieved a wide bandwidth by improving the ground plane. [ABSTRACT FROM AUTHOR]

Several deep learning models are presented: Ref. [[4]] designs a network intrusion detection model, DLNID, that combines an attention mechanism, a bidirectional long short-term memory (LSTM), and an adaptive synthetic sampling, and demonstrates improved performance for severely imbalanced data. In addition, there are a couple of papers on HPO: Ref. [[11]] introduces a greedy I k i -fold cross validation method that vastly reduces the average time required to find the best-performing model with or without a computational budget, and experimentally verifies its improved performance over existing methods. Since its inception as a branch of Artificial Intelligence, Machine Learning (ML) has flourished in recent years. [Extracted from the article]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

This editorial presents a summary of the Special Issue on Artificial Intelligence Solutions and Applications for Distributed Systems in Smart Spaces, presented in the "Computer Science & Engineering" section of I Electronics i (ISSN 2079-9292). The application of artificial intelligence (AI) in distributed environments has become a research area of high-added value and economic potential. The Present Issue This Special Issue consists of seven papers that address pivotal topics in the field of AI solutions for distributed systems and their applications. [Extracted from the article]

This paper aims at evaluating the current state of research into artificial intelligence (AI)-based decision support systems (DSS) for improving construction project sustainability. The literature was systematically reviewed to explore the use of AI in the construction project lifecycle together with the consideration of the economic, environmental, and social goals of sustainability. A total of 2688 research papers were reviewed, and 77 papers were further analyzed, and the major tasks of the DSSs were categorized. Our review results suggest that the main research stream is dedicated to early-stage project prediction (50% of all papers), with artificial neural networks (ANNs) and fuzzy logic (FL) being the most popular AI algorithms in use. Hybrid AI models were used in 46% of all studies. The goal for economic sustainability is the most considered in research, with 87% of all papers considering this goal, and there is evidence given of a trend towards the environmental and social goals of sustainability receiving increasing attention throughout the latter half of the decade. [ABSTRACT FROM AUTHOR]

Simple Summary: Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms "machine learning" and "mastitis" were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return. Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area. [ABSTRACT FROM AUTHOR]

Today, the field of biomedical engineering spans numerous areas of scientific research that grapple with the challenges of intelligent analysis of small datasets. Analyzing such datasets with existing artificial intelligence tools is a complex task, often complicated by issues like overfitting and other challenges inherent to machine learning methods and artificial neural networks. These challenges impose significant constraints on the practical application of these tools to the problem at hand. While data augmentation can offer some mitigation, existing methods often introduce their own set of limitations, reducing their overall effectiveness in solving the problem. In this paper, the authors present an improved neural network-based technology for predicting outcomes when analyzing small and extremely small datasets. This approach builds on the input doubling method, leveraging response surface linearization principles to improve performance. Detailed flowcharts of the improved technology's operations are provided, alongside descriptions of new preparation and application algorithms for the proposed solution. The modeling, conducted using two biomedical datasets with optimal parameters selected via differential evolution, demonstrated high prediction accuracy. A comparison with several existing methods revealed a significant reduction in various errors, underscoring the advantages of the improved neural network technology, which does not require training, for the analysis of extremely small biomedical datasets. [ABSTRACT FROM AUTHOR]

This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems. [ABSTRACT FROM AUTHOR]

Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector's concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements. [ABSTRACT FROM AUTHOR]

Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI. [ABSTRACT FROM AUTHOR]

In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network's weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0 % . [ABSTRACT FROM AUTHOR]

Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings. [ABSTRACT FROM AUTHOR]

The adoption of renewable energy sources, particularly wind energy, has seen remarkable growth in recent years. Wind energy generation requires efficient and reliable generators that can convert wind energy into usable electrical energy. Switched Reluctance Generators (SRGs) have been gaining popularity in wind energy applications due to their high efficiency, reliability, and low maintenance requirements. The multiphase switched reluctance generators are used especially in wind power plants due to their ability to operate in variable speed ranges. In this paper, a MATLAB/SIMULINK based model of Switched Reluctance Generator (SRG) is presented. The obtained results show good agreement under different operating conditions. The obtained results (current, voltage, torque, speed, flux, and angle of generator) are shown in graphs. [ABSTRACT FROM AUTHOR]

This paper represents the ways in which a secure and reliable voting machine could be implemented using hardware descriptive language, which in this case is Verilog. In order to emphasize security, the proposed design is implemented in two ways. The first variant is a basic version comprising some basic safety protocols; however, the subsequent variant is a safer kind of FPGA-based voting machine. Furthermore, concrete illustration is presented using the Vivado and Artix-7-based FPGA Basys-3. Finally, the obtained conclusions seem to be encouraging to a certain extent, as, evidently, the system provides robust and assured voting, along with minimizing latency and power consumption. In addition to this, this design could be utilized as a starting point for the development of superior voting systems utilizing FPGAs in the future. [ABSTRACT FROM AUTHOR]

The degradation of Zinc Oxide Surge Arresters (ZnOSA) is commonly detected by the resistive component of its leakage current 'Ir'. The methods previously employed to extract the resistive component of the leakage current were intricate and demanded a greater number of computational steps. The objective of this paper is to propose a new technique for the age detection and degradation analysis of ZnOSA, named the 'Correlation Method', which does not require the extraction of the resistive leakage current. The correlation coefficient is calculated between the power factor (pf) and total leakage current (It) on the MATLAB software (Version R2018a). The performance of this method is analyzed experimentally by changing the voltage, ranging from 70 to 120 kV, of surge arresters. The proposed research offers improved computational efficiency and provides valuable insights into the relationship between the power factor, leakage current, and the aging process of ZnOSA. Moreover, the results of the proposed methods are compared with those of existing techniques. [ABSTRACT FROM AUTHOR]

A common medium access control (MAC) protocol used in Wi-Fi networks is the distributed coordination function (DCF). In the past few decades, a number of network simulators have been developed to validate their analytical models for DCF and other MAC protocols, among which OMNET has gained popularity. NS3 and OMNET provide fundamental modeling framework for the IEEE 802.11 MAC and PHY layers. In this paper, we conducted a comparative study of a unified IEEE 802.11 DCF analytical model with NS3 and OMNET for performance evaluation. Various access modes and different topologies were examined for comparing the theoretical study with the simulators. This study provided a basis that the results obtained from NS3 and OMNET Wi-Fi modules are also credible just like in the theory. [ABSTRACT FROM AUTHOR]

The paper presents a compact dual-band pentagonal-shaped patch antenna, with resonating frequencies 7.6 GHz and 12 GHz, that is used for satellite communication, 5G application, and high-speed applications. The proposed antenna is designed on an FR4 substrate with an overall dimension of 16 x 16 mm where the height of the substrate is 0.8 mm. The proposed antenna contains a pentagonal-shaped patch with four circles at each corner of the patch that is used for the dual-band; in addition, the circular slot with the combination of the cross-shaped slot achieves better results. The proposed antenna has a partial ground configuration for better bandwidth and gain. The peak gain of the antenna is 8.07 dB at 12 GHz and has a 7.13 dB gain at 7.6 GHz. With its compact size and wide bandwidth capabilities, the antenna proves to be an ideal solution for mobile communication systems, satellite ground stations, and remote sensing applications across C-, X-, and Ku-bands. The proposed antenna also covers Ku used for satellite and high-speed communication. [ABSTRACT FROM AUTHOR]

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Risk assessment in early breast cancer is critical for clinical decisions, but defining risk categories poses a significant challenge. The integration of conventional histopathology and biomarkers with artificial intelligence (AI) techniques, including machine learning and deep learning, has the potential to offer more precise information. AI applications extend beyond detection to histological subtyping, grading, and molecular feature identification. The successful integration of AI into clinical practice requires collaboration between histopathologists, molecular pathologists, computational pathologists, and oncologists to optimize patient outcomes. Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of deep neural network (DNN)-based signal detection in multiple input, multiple output (MIMO), communication systems. MIMO technology plays a critical role in achieving high data rates and improved capacity in modern wireless communication standards like 5G. However, signal detection in MIMO systems presents significant challenges due to channel complexities. This study conducts a comparative analysis of signal detection techniques within both the single input, single output (SISO), and MIMO frameworks. The analysis focuses on the entire transmission chain, encompassing transmitters, channels, and receivers. The effectiveness of three traditional methods—maximum likelihood detection (MLD), minimum mean square error (MMSE), and zero-forcing (ZF)—is meticulously evaluated alongside a novel DNN-based approach. The proposed study presents a novel DNN-based signal detection model. While this model demonstrates superior computational efficiency and symbol error rate (SER) performance compared to more conventional techniques like MLD, MMSE, and ZF in the context of a SISO system, MIMO systems face some challenges in outperforming the conventional techniques specifically in terms of computation times. This complexity of MIMO systems presents challenges that the current DNN design has yet to fully address, indicating the need for further developments in wireless communication technology. The observed performance difference between SISO and MIMO systems underscores the need for further research on the adaptability and limitations of DNN architectures in MIMO contexts. These findings pave the way for future explorations of advanced neural network architectures and algorithms specifically designed for MIMO signal-processing tasks. By overcoming the performance gap observed in this work, such advancements hold significant promise for enhancing the effectiveness of DNN-based signal detection in MIMO communication systems. [ABSTRACT FROM AUTHOR]

Background and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine. [ABSTRACT FROM AUTHOR]

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research. [ABSTRACT FROM AUTHOR]

This paper highlights 20 significant problems in AI research, with potential solutions via the SP Theory of Intelligence (SPTI) and its realisation in the SP Computer Model. With other evidence referenced in the paper, this is strong evidence in support of the SPTI as a promising foundation for the development of human-level broad AI, aka artificial general intelligence. The 20 problems include: the tendency of deep neural networks to make major errors in recognition; the need for a coherent account of generalisation, over- and under-generalisation, and minimising the corrupting effect of 'dirty data'; how to achieve one-trial learning; how to achieve transfer learning; the need for transparency in the representation and processing of knowledge; and how to eliminate the problem of catastrophic forgetting. In addition to its promise as a foundation for the development of AGI, the SPTI has potential as a foundation for the study of human learning, perception, and cognition. And it has potential as a foundation for mathematics, logic, and computing. [ABSTRACT FROM AUTHOR]

Featured Application: A review of Artificial Intelligence-driven approaches to 3D printing of large-scale architectural structures can provide practitioners and academic researchers with a comprehensive understanding of the current state of the field, reinforce innovative design, inform material and fabrication method choices, support sustainability goals, and provide practical insights through the review of different cases. Artificial Intelligence (AI) and 3D printing (3DP) play considerable roles in what is known as the Fourth Industrial Revolution, by developing data- and machine-intelligence-based integrated production technologies. In architecture, this shift was induced by increasingly complex design requirements, posing important challenges for real-world design implementation, large-scale structure fabrication, and production quality standardization. The study systematically reviews the application of AI techniques in all stages of creating 3D-printed architectural structures and provides a comprehensive image of the development in the field. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3D-printed structures; and (4) identify research gaps and provide recommendations for future research. The findings indicate that AI is an emerging addition to the 3DP process, mainly transforming it through the real-time adjustment of the design or printing parameters, enhanced printing quality control, or prediction and optimization of key design features. However, the potential of the application of AI in large-scale architectural 3D printing still needs to be explored. Lastly, the study emphasizes the necessity of redefining traditional field boundaries, opening new opportunities for intelligent architectural production. [ABSTRACT FROM AUTHOR]

The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods. [ABSTRACT FROM AUTHOR]

This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids' complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

Recent advancements in cybersecurity threats and malware have brought into question the safety of modern software and computer systems. As a direct result of this, artificial intelligence-based solutions have been on the rise. The goal of this paper is to demonstrate the efficacy of memory-optimized machine learning solutions for the task of static analysis of software metadata. The study comprises an evaluation and comparison of the performance metrics of three popular machine learning solutions: artificial neural networks (ANN), support vector machines (SVMs), and gradient boosting machines (GBMs). The study provides insights into the effectiveness of memory-optimized machine learning solutions when detecting previously unseen malware. We found that ANNs shows the best performance with 93.44% accuracy classifying programs as either malware or legitimate even with extreme memory constraints. [ABSTRACT FROM AUTHOR]

Multifunctional composites provide more than one function from the same part. The anisotropy, material, and process characterization challenges and the lack of standardization on the 3D-printed multifunctional carbon composites make it difficult for application into aerospace. The current solutions for additive manufacturing (AM) technologies and additively manufactured monofunctional and multifunctional composites are not mature enough for safety-critical applications. A new approach is proposed to explore the use of machine learning (ML) in the design, development, AM, testing, and certification of multifunctional composites for aircraft, unmanned aircraft systems (UAS), and spacecraft. In this work, an artificial neural network (ANN) architecture is proposed. An AM-embedded building block approach integrates the complete lifecycle of aircraft, UAS, and spacecraft using ANN to support the continued operational safety (COS) of aircraft, spacecraft, and UAS. The proposed method exploits the power of ANN on the metadata for the characterization of multifunctional material properties and processes and the mapping of the failure modes compared with the predicted models and history. This paper provides an in-depth analysis and explanation of the new methods needed to overcome the existing barriers, problems, and situations. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery. [ABSTRACT FROM AUTHOR]

Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is one of the most promising technologies based on machine learning algorithms. In this paper, we propose a workflow for the implementation of deep neural networks. This workflow attempts to combine the flexibility of high-level compilers (HLS)-based networks with the architectural control features of hardware description languages (HDL)-based flows. The architecture consists of a convolutional neural network, SqueezeNet v1.1, and a hard processor system (HPS) that coexists with acceleration hardware to be designed. This methodology allows us to compare solutions based solely on software (PyTorch 1.13.1) and propose heterogeneous inference solutions, taking advantage of the best options within the software and hardware flow. The proposed workflow is implemented on a low-cost field programmable gate array system-on-chip (FPGA SOC) platform, specifically the DE10-Nano development board. We have provided systolic architectural solutions written in OpenCL that are highly flexible and easily tunable to take full advantage of the resources of programmable devices and achieve superior energy efficiencies working with a 32-bit floating point. From a verification point of view, the proposed method is effective, since the reference models in all tests, both for the individual layers and the complete network, have been readily available using packages well known in the development, training, and inference of deep networks. [ABSTRACT FROM AUTHOR]

The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures. [ABSTRACT FROM AUTHOR]

The farming of ground birds requires extensive labor for the timely feeding and watering of the birds, in addition to cleaning their manure. An autonomous farming system can not only reduce labor costs but can also ensure timely feeding and automatic watering. Moreover, IoT connectivity can help the farmer keep an eye on the birds while physically being away from the farming site. Inspired by this concept, this study presents the design and implementation of a smart autonomous cage for the farming of ground birds. A design of sensors based on ambient temperature and an air quality monitoring system, the mechatronic design of autonomous egg collection and a manure cleaning system are presented in this paper. A novel system control algorithm for the autonomous control of the smart cage is also presented. [ABSTRACT FROM AUTHOR]

Technical losses in transmission systems are crucial for short-term planning and decision-making, particularly in complex systems. Traditional deterministic methods like Newton-Raphson prove ineffective in handling systems with a large number of buses and intricate topologies. Consequently, there is a growing interest in employing heuristic and intelligent algorithms to achieve faster and more accurate technical loss estimations. This paper introduces the Neural Network Prognosis Algorithm (NNPA), a simple and robust technique that exclusively relies on technical parameters derived from a Newton-Raphson-based load flow analysis, specifically active and reactive power, for 22 buses. The algorithm demonstrates promising progress, exhibiting notable convergence toward the desired mean squared error and achieving a commendable correlation coefficient of 0.999. Despite incorporating randomized data points with Gaussian noise, the algorithm's results present compelling evidence of its effectiveness and validation. [ABSTRACT FROM AUTHOR]

The classical problem of islanding detection in distributed generation falls into the commonly used categories known as passive, active, and hybrid techniques. These approaches vary in terms of their accuracy, security, and dependability. Detecting islanding in modern inverter-based distribution systems is of the utmost importance to ensuring the protection of equipment, ensuring the safety of workers, and preventing operational and cascaded faults when the system is specially subjected to renewable integration. This research paper presents a technical comparison of the aforementioned techniques, discussing their detection rate, Non-Detection Zone (NDZ), distinct topologies, and their effectiveness in integration for low-frequency grids. The review offers a thorough analysis comparing the key attributes found in the current literature while also highlighting the forthcoming needs for advanced management, optimization, and control technologies. These technologies are crucial to effectively tackling the difficulties that arise from integrating renewable energy sources into established grid systems. [ABSTRACT FROM AUTHOR]

Drowsiness has become a significant contributing factor to traffic accidents in modern times, posing a major concern to society. Driver fatigue or sleepiness leads to decreased reaction time, diminished attention, and compromised decision-making abilities, thereby affecting the overall driving experience. This paper addresses this issue by proposing a drowsiness detection system based on image processing, utilizing a cascade of classifiers built on Haar-like features. The system effectively detects the eyes, allowing for determination of eye closure or opening, which serves as an indicator of driver drowsiness. [ABSTRACT FROM AUTHOR]

Pakistan is facing the deepest energy and economic crisis of its history. In fact, the ongoing economic crisis is more or less due to the energy crisis. In spite of this critical situation, Pakistan began from a meager 70 MW installed capacity at the time of the creation of the country and now has raised that capacity to 40,923 MW with a huge transmission network infrastructure based on 58,679 km transmission lines and a consumer base of 36.5 million. Despite this massive progress, there is a continued power deficit, mounting circular debt, and large losses, which all indicate a depleted picture of the power sector. This paper primarily undertakes the diagnosis of these crises and provides a basic assessment of demand-side management as a potential avenue to overcome energy crises. In this context, a detailed overview of the energy and power sectors of Pakistan, including the outdated T&D system, is undertaken. These diagnoses suggest that poor administration, governance, and inappropriate policies have contributed to these crises. In the meantime, efforts to overcome these crises with expensive capacity additions have also failed to address the energy crisis. However, a careful review of the literature and on-the-ground matters indicates that DSM is the most reliable solution. Sectoral DSM potential is estimated. Implementing the proposed measures will help greatly to overcome these crises. [ABSTRACT FROM AUTHOR]

It has been observed from many pieces of research and through applications that robotic movements using human interaction are considered dangerous, tiresome and require extraordinary precision and smooth control. Specifically, medical and healthcare applications have been the highest priority in recent years. The concept of rehabilitation using robotics was introduced during the 1980s with the motive of freeing therapists from repetitive work while treating an increasing elderly population requiring physiotherapy. Furthermore, the consistency of the robot's operation and the volume of repetitions has increased. They can assist therapists in performing tedious tasks and let them concentrate on several patients simultaneously. Several types of rehabilitation robot devices have been produced in recent years with different modes of training and control strategies using various control algorithms. In this research paper, a comprehensive overview of rehabilitation in relation to robotics is presented. The main aim is to determine robust controlling optimization for the smooth control of robotic movement, as these movements require a lot of precision and accuracy. The analysis showed that M-PSO was found to be very effective and robust in finding the best optimal values, as the Modified PSO achieved the minimum root mean square value and a best fit of 98.7. [ABSTRACT FROM AUTHOR]

With the increasing predilection for renewable energy sources across the world, the novel idea of a miniature version of a grid, called a microgrid, has emerged. The efficiency and sustainability of a power grid increase by integrating distributed energy resources (DERs). However, designing an optimum protection scheme has become a substantial challenge due to bi-directional power flows and varying fault levels in the microgrid with distributed energy resources (DERs). The existing protection strategies are not capable of dealing with the different operational states and natures of DERs. Therefore, modifications to the conventional protection schemes are required to benefit from the advantages of de-centralized power generation. Optimum co-ordination between the protection devices (PDs) is needed to achieve fast, secure, and reliable protection of the system. This paper proposes a protection philosophy for a renewable-based AC microgrid and validates its resilience by analyzing the response of the system in different faulty scenarios. Moreover, a genetic algorithm (GA) is used to optimize the proposed protection scheme to achieve a cost-effective, resilient, reliable, and long-term solution for sustainable power generation. [ABSTRACT FROM AUTHOR]

Distribution system parameter estimation plays a vital role in the overall control and monitoring of the network. A conventional technique to obtain information about the system voltages and line currents involves iterative load flow procedures. Such methods sometimes do not converge due to the limited number of available measurements. This paper proposes a simulated data-driven approach for estimating feeder loadings under various operating scenarios of the IEEE 15 bus system. A deep neural network using the MATLAB nntool is implemented, and promising results are presented. [ABSTRACT FROM AUTHOR]

The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power demand for the Polish National Power Grid for the next day is approx. 1%; therefore, the main objective of the review is to verify whether it is possible to improve efficiency while maintaining the minimum financial outlays and time-consuming efforts. The methods that fulfil these conditions are autoregressive methods; therefore, the paper focuses on autoregressive methods, which are less time-consuming and, as a result, cheaper in development and applications. The prepared review ranks the forecasting models in terms of the forecasting effectiveness achieved in the literature on the subject, which enables the selection of models that may improve the currently achieved effectiveness of the transmission system operator. Due to the applied approach, a transparent set of forecasting methods and models was obtained, in addition to knowledge about their potential in the context of the needs for short-term forecasting of electricity demand in the national power system. The articles in which the MAPE error was used to assess the quality of short-term forecasts were analyzed. The investigation included 47 articles, several dozen forecasting methods, and 264 forecasting models. The articles date from 1997 and, apart from the autoregressive methods, also include the methods and models that use explanatory variables (non-autoregressive ones). The input data used come from the period 1998–2014. The analysis included 25 power systems located on four continents (Asia, Europe, North America, and Australia) that were published by 44 different research teams. The results of the review show that in the autoregressive methods applied to forecasting short-term power demand, there is a potential to improve forecasting effectiveness in power systems. The most promising prognostic models using the autoregressive approach, based on the review, include Fuzzy Logic, Artificial Neural Networks, Wavelet Artificial Neural Networks, Adaptive Neurofuse Inference Systems, Genetic Algorithms, Fuzzy Regression, and Data Envelope Analysis. These methods make it possible to achieve the efficiency of short-term forecasting of electricity demand with hourly resolution at the level below 1%, which confirms the assumption made by the authors about the potential of autoregressive methods. Other forecasting models, the effectiveness of which is high, may also prove useful in forecasting by electricity system operators. The paper also discusses the classical methods of Artificial Intelligence, Data Mining, Big Data, and the state of research in short-term power demand forecasting in power systems using autoregressive and non-autoregressive methods and models. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as 'bradykinesia', loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation. [ABSTRACT FROM AUTHOR]

The use of adhesive joints has become increasingly popular in various industries due to their many benefits, such as low weight and good mechanical performance. However, adhesive joints can suffer from defects, one of them being weak adhesion. This defect poses a significant risk to structural integrity and can lead to premature failure, but is hard to detect using existing nondestructive testing methods. Therefore, there is a need for an effective technique that can detect weak adhesion in single-lap joints (SLJ) to prevent failure and assist in maintenance, namely in the framework of structural health monitoring. This paper presents a novel approach utilizing machine learning and Lamb Waves (LW) to determine the level of weak adhesion. Firstly, a numerical model of SLJs with different levels of weak adhesion is created and an original approach is proposed for its validation with data from real samples so that reliable LW data can further be easily generated to train and test any other data-driven algorithm for tackling damage. Secondly, a damage detection method is proposed, based on artificial neural networks and fed with simulated data, to determine the level of damage in SLJs, independent of their location. The results show that the simulation model can be validated with a small set of experimental data, being capable of replicating real damage in SLJs. Additionally, the use of simulated data in the training algorithm can increase the accuracy of the simulation model up to 26% when compared to only considering experimental data. The adopted artificial neural network for detecting weak adhesion emerges as a promising approach, yielding a precision of over 95%. Thus, machine learning and LW data can be used to improve the reliability and accuracy of adhesive bonding quality control, as well function as a technique for structural health monitoring, which can enhance the safety and durability of bonded structures. [ABSTRACT FROM AUTHOR]