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De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

To investigate how the petroleum industry evaluates and selects powerful cloud service providers, first, an evaluation index system including 25 indices such as scalability and private data protection is built. This index system can systematically examine the comprehensive strengths of cloud service providers. Aiming to solve the problems that the traditional expert evaluation method has high requirements on expert experience and is easily affected by subjective factors, a novel artificial intelligence evaluation model named TentISSA-BPNN is proposed. The objective evaluation ability of this model can be effectively used in evaluation research on cloud service providers for petroleum enterprises. In this model, the SSA algorithm is optimized by Tent chaotic mapping and adaptive inertia weight; an algorithm, TentISSA, that has good stability and fast convergence speed is designed and proposed; and the BPNN is improved with TentISSA to obtain more accurate evaluation results. To evaluate the performance of the TentISSA algorithm, nine unimodal and multimodal functions are selected in this paper to test the convergence accuracy. Then, seven models are selected as the control groups to validate the effectiveness and performance of the TentISSA-BPNN evaluation model proposed in this paper. Finally, the preprocessed data of the candidate cloud service providers are input into the trained neural network model proposed in this paper for evaluation. Based on the ranking of the evaluation scores, the comprehensive strengths of the cloud service providers are obtained to provide a decision-making reference for managers of petroleum enterprises in the process of choosing cloud service providers. [ABSTRACT FROM AUTHOR]

One key frontier of artificial intelligence (AI) is the ability to comprehend research articles and validate their findings, posing a magnanimous problem for AI systems to compete with human intelligence and intuition. As a benchmark of research validation, the existing peer-review system still stands strong despite being criticized at times by many. However, the paper vetting system has been severely strained due to an influx of research paper submissions and increased conferences/journals. As a result, problems, including having insufficient reviewers, finding the right experts, and maintaining review quality, are steadily and strongly surfacing. To ease the workload of the stakeholders associated with the peer-review process, we probed into what an AI-powered review system would look like. In this work, we leverage the interaction between the paper's full text and the corresponding peer-review text to predict the overall recommendation score and final decision. We do not envisage AI reviewing papers in the near future. Still, we intend to explore the possibility of a human–AI collaboration in the decision-making process to make the current system FAIR. The idea is to have an assistive decision-making tool for the chairs/editors to help them with an additional layer of confidence, especially with borderline and contrastive reviews. We use a deep attention network between the review text and paper to learn the interactions and predict the overall recommendation score and final decision. We also use sentiment information encoded within peer-review texts to guide the outcome further. Our proposed model outperforms the recent state-of-the-art competitive baselines. We release the code of our implementation here: https://github.com/PrabhatkrBharti/PEERRec.git. [ABSTRACT FROM AUTHOR]

The health management of railway signal equipment in the high-speed railway is a key link between intelligent operation and maintenance. Accurately predicting the health state of compensation capacitors is of great significance to ensure the reliable work of track circuits. This paper proposes an improved deep neural network algorithm focusing on the problem of long-term accurate health forecasts for compensation capacitors. First, establishing a transmission state model for degradation mechanism mining, the difference function that can quantitatively evaluate features is defined by piecewise processing cab signaling receiving voltage. Introducing the degradation model, predictive driving under both model and data is implemented. Then, the convolutional neural networks and bidirectional long–short-term memory are combined and improved to construct a novel artificial intelligence combination strategy, while parameters are optimized based on the sparrow search algorithm. Finally, facing the conditional repair of compensation capacitors, we set a reasonable threshold for the occurrence of hidden dangers to complete fault warning. This novel and practical approach effectively explores the procedure of prognosis and health management, while the refined maintenance will better utilize current monitoring information, helping the intelligence and accuracy of safety control decision-making. [ABSTRACT FROM AUTHOR]

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) in information technology is a system of hardware or software modeled after the operation of neurons in the human brain. ANNs, often known as "neural networks," are a form of deep learning technology that falls under the umbrella of Artificial Intelligence (AI). Commercial applications of these technologies typically focus on optimization and solving complex signal processing and pattern recognition problems. Multiple types of optimization techniques are utilized to determine the optimal neural network for a model. These procedures help determine and define the model's accuracy, dependability, functionality, and capacity. The convergence of the neural network helps determine the number of training iterations required to generate the fewest errors. In this paper, we investigate an activation function to help reduce the training time of the analog-to-digital converter (ADC). A new Hopfield ADC model is proposed by using the proteretic activation function property. We supported our research by simulating the new ADC converter and comparing the traditional Hopfield ADC, the hysteretic ADC, and the proteretic ADC. Experiment and simulation demonstrate that the proteretic function provides a faster rate of convergence than other functions, thereby enhancing the performance of the ADC application. [ABSTRACT FROM AUTHOR]

Due to high spatial and temporal resolution, geostationary meteorological satellite imagery is a valuable source of information on the development of deep convective clouds and related severe weather events. Some methods for automatic deep convection detection from satellite data provide a satisfactory probability of detection for independent datasets, but are characterized by a high false alarm rate. The paper gives a description of an algorithm for automatic detection of deep convective clouds with satellite imagery using gradient boosting, logistic regression, and artificial neural network models. The results of validation of the proposed method using dependent and independent data of ground-based observations for the period 2013–2020 are presented. A low false alarm rate and high probability of detection suggest that the algorithm can be used in the operational mode. [ABSTRACT FROM AUTHOR]

Integrated circuit (IC) testing presents complex problems that for large circuits are exceptionally difficult to solve by traditional computing techniques. To deal with unmanageable time complexity, engineers often rely on human "hunches" and "heuristics" learned through experience. Training computers to adopt these human skills is referred to as machine intelligence (MI) or machine learning (ML). This survey examines applications of such methods to test analog, radio frequency (RF), digital, and memory circuits. It also summarizes ML applications to hardware security and emerging technologies, highlighting challenges and potential research directions. The present work is an extension of a recent paper from IEEE VLSI Test Symposium (VTS'21), and includes recent applications of artificial neural network (ANN) and principal component analysis (PCA) to automatic test pattern generation (ATPG). [ABSTRACT FROM AUTHOR]

Manual calculation of the compressive strength of concrete (CSC) is an expensive and time-consuming process. Soft computing methods outperform the statistical methods used to resolve these problems. Nonetheless, complicated prediction models are still incomplete and require more exploration. Artificial neural networks (ANNs) provide a better and faster technique featuring solitary hidden layers and have improved the generalization capacity. The present paper presents three ANN-based (shuffled complex evolution, evaporation rate based water cycle algorithm (ERWCA), and Cuckoo optimization algorithm) prediction models to anticipate the compressive strength of concrete efficiently. An available database from the UCI repository is employed to develop and access the model performance. A comparison is made between the prediction accuracies of the above three techniques. Using all models, a comparative investigation has been conducted to predict the compressive strength of concrete at the curing ages of 91, 56, and 28 days. The experimental findings obtained from the ERWCA-MLP method indicate its capability of robust CSC prediction. On average, this method achieves the minimum RMSE of 0.55314 and 0.43329 and R2 of 0.99803 and 0.99824. The statistical significance test and the comparative analysis of simulation results indicate the superiority of ERWCA-MLP in predicting the compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Semantic relatedness is one of the most significant aspects of natural language processing. It has been identified as a critical technology for developing intelligent systems like Siri, Microsoft Ice, Cortana, and Xiaoai. In 2014, SemEval ranked SR as the top task. While many existing studies have focused on analyzing the entailment of single phrases, advancements in deep learning have made it possible to analyze complete sentences or texts. While the natural parallelism of membrane computing has shown promise for data processing, harnessing this potential to advance semantic relatedness remains an open problem yet to be tackled. This paper proposes a novel Siamese Manhattan LSTM-SNP approach (SiMaLSTM-SNP) for the SR problem. The approach uses a collaborative Word2vec and 10-Layer Attention strategy to represent and extract sentence pairs and a Siamese LSTM-SNP structure to calculate the hidden states of sentences. The multi-head self-attention layer identifies text associations and redistributes hidden state weights. The last hidden state is extracted, and the relatedness score is calculated using the Manhattan distance. The experiments demonstrate that SiMaLSTM-SNP outperforms 17 classical SR baselines and 7 novel approaches on the standard datasets SICK and STS in terms of mean square error performance. This indicates that SiMaLSTM-SNP can accurately capture the semantic distinction between two sentences and effectively preserve their semantic information. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is being employed not only in businesses, entertainment, and academia but also in sports training to raise the level of competition among athletes. Artificial neural networks (ANNs) are the brains of modern AI, and this paper examines their application in sports training's load prediction with the hope of improving athletic performance, creating scientifically efficient training programs, and increasing the accuracy of training load forecasts. First, a sports training analysis system based on Browser Server (B/S) is constructed, and the overall structure, specific function modules, and database of the system are designed and introduced. Then, the artificial fish swarm algorithm (AFSA)-based prediction and modeling approach of sports training's load is applied. To put it more specifically, we obtained a comprehensive index of joint acceleration in sports training by using data acquisition devices such as sensors. The independent variable in the load prediction is defined by the computational results, which are combined with other relevant features to obtain the load characteristics of sports training. The initial weights and thresholds of the ANN are optimized with the help of AFSA. To produce the final prediction result, the optimized ANN and extracted load characteristics are coupled with the prediction equation. The prediction model is applied to the sports training analysis system for realizing the application of training load prediction. Experiments show that the methods proposed in this paper can effectively improve the accuracy and efficiency of sports training and load prediction, improve the effect of athletes' training, and thus enhances the level of sports competitiveness. [ABSTRACT FROM AUTHOR]

The prediction of wildfire spreading is necessary for managing and fighting the forest fire. The traditional models require higher accuracy of the input parameters, which is impossible in real forest fires. The paper proposed a fire-spreading model based on the dynamic data of the fire field to improve its adaptability. The model is designed using a convolutional neural network with mixed-inputs and attention mechanisms (MI-AM-CNN). It predicts the burn map after a period of time through the multiple-channel image containing terrain variables and the current burn map, and the scalars containing climate variables. The channel and spatial attention modules are integrated to handle the advanced features that contain important fire variables information and strengthen the influence of important features on the prediction. Based on the FARSITE, a large number of data sets are generated for training, validating, and testing the models in the paper. The proposed model MI-AM-CNN is compared with the state-of-the-art neural network models. Quantitative results show that MI-AM-CNN has the highest performance in predicting effectiveness and efficiency, and it can be applied recursively to get the continuous predicted results. In addition, the prediction results of MI-AM-CNN on the historical fire data demonstrate the ability of its application in the real fire case. The MI-AM-CNN can be used as a predictive method in firefighting operations, and its predicted results can provide theoretical support for the forest fire spread prediction method based on artificial intelligence. [ABSTRACT FROM AUTHOR]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [ABSTRACT FROM AUTHOR]

The technology of big data analysis and artificial intelligence deep learning has been actively cross-combined with various fields to increase the effect of its original low single field. Precision components commonly used in electronic products use changes in the conductivity of semiconductors to process information. This study aims to review key milestones and recent developments in the semiconductor industry using artificial intelligence methods. For this systematic review, we searched academic networks between 2015 and 2022, including Nature, Elsevier, Springer, Taylor & Francis Online, Multidisciplinary Digital Publishing Institute, and the Institute of Electrical and Electronics Engineers. The literature reviewed is based on conference proceedings and journal articles, specifically covering the key achievements of the discussion paper, the key technologies used, experimental results, opportunities, and future research pathways. After searching on an academic website, we selected six major studies. In five of these studies, visual object detection, surface defect detection, machine production scheduling application, fault diagnosis and prediction, and monitoring of the manufacturing process were made using artificial neural networks, machine learning methods, and hybrid models. In addition, the studies covered independent, single methods or used more than two types of technologies for performance comparison. Finally, we reviewed the strengths and weaknesses of the literature. We also analysed various datasets, acquisition systems, and experimental scenarios. The review shows that as the number of studies conducted in manufacturing continues to increase, more research is needed to unearth key information that is often overlooked, all of which are challenges in refining science and overcoming real-world scenarios. [ABSTRACT FROM AUTHOR]

In the era of artificial intelligence, trained neural network models have become new products of the information age. Most of machine learning strategies currently used to train neural networks are supervised learning, and thus, training data with labels become new intellectual capital (IC). Due to commercial confidentiality, data cannot be shared directly among information companies, which in turn prevents them from integrating resources to train better models. We need a framework that encrypts neural network models trained on the data and provides certain model exchange rewards that can be used to incentivize data sharing and to protect intellectual property (IP) and privacy of intellectual capital. Currently, federated learning provides a framework to train neural networks without compromising privacy, while block chain–based trading systems can attract other participants through a reward mechanism set by smart contracts. In this paper, we propose a block chain–based federated learning algorithm that enables reliable data sharing while protecting data from leakage, and design smart contracts based on the incentive mechanism of Shapley Values to reward data providers. We design a platform for managing IC by combining federated learning and block chain called Federated Learning Intellectual Capital Platform (FedLICP). [ABSTRACT FROM AUTHOR]

Virtue ethics has many times been suggested as a promising recipe for the construction of artificial moral agents due to its emphasis on moral character and learning. However, given the complex nature of the theory, hardly any work has de facto attempted to implement the core tenets of virtue ethics in moral machines. The main goal of this paper is to demonstrate how virtue ethics can be taken all the way from theory to machine implementation. To achieve this goal, we critically explore the possibilities and challenges for virtue ethics from a computational perspective. Drawing on previous conceptual and technical work, we outline a version of artificial virtue based on moral functionalism, connectionist bottom–up learning, and eudaimonic reward. We then describe how core features of the outlined theory can be interpreted in terms of functionality, which in turn informs the design of components necessary for virtuous cognition. Finally, we present a comprehensive framework for the technical development of artificial virtuous agents and discuss how they can be implemented in moral environments. [ABSTRACT FROM AUTHOR]

Market risk management of financial derivatives requires the efficient calculation of their price sensitivities with respect to changes in market factors. This paper shows how a deep feed-forward neural network which has been trained for pricing derivative instruments can be efficiently used to calculate these sensitivities as well. The proposed method is a fast and easily implementable alternative approach to automatic differentiation, and it simultaneously calculates all the first- and second-order derivatives of a multilayer feed-forward neural network with respect to its input features. The paper quantifies the performance improvement of the proposed method over a recent, publicly available implementation of automatic differentiation for a wide range of network sizes. The number of input parameters in these networks corresponds to those of commonly used financial models with stochastic volatility. The numerical accuracy of the proposed sensitivity calculations is demonstrated with a case study, calculating price sensitivities of European options under stochastic volatility. While the paper focuses on financial applications, the results presented herein are applicable to all deep feed-forward neural networks with sufficiently smooth activation functions. [ABSTRACT FROM AUTHOR]

Deep learning has been widely applied in solving many tasks, such as image recognition, speech recognition, and natural language processing. It requires a high-quality dataset, advanced expert knowledge, and enormous computation to train a large-scale Deep Neural Network (DNN) model, which makes it valuable enough to be protected as Intellectual Property (IP). Defending DNN models against IP violations such as illegal usage, replication, and reproduction is particularly important to the healthy development of deep learning techniques. Many approaches have been developed to protect the DNN model IP, such as DNN watermarking, DNN fingerprinting, DNN authentication, and inference perturbation. Given its significant importance, DNN IP protection is still in its infancy stage. In this paper, we present a comprehensive survey of the existing DNN IP protection approaches. We first summarize the deployment mode for DNN models and describe the DNN IP protection problem. Then we categorize the existing protection approaches based on their protection strategies and introduce them in detail. Finally, we compare these approaches and discuss future research topics in DNN IP protection. [ABSTRACT FROM AUTHOR]

Academic performance, a globally understood metric, is utilized worldwide across disparate teaching and learning environments and is regarded as a quantifiable indicator of learning gain. The ability to reliably estimate student's academic performance is important and can assist academic staff to improve the provision of support. However, it is recognized that academic performance estimation is non-trivial and affected by multiple factors, including a student's engagement with learning activities and their social, geographic, and demographic characteristics. This paper investigates the opportunity to develop reliable models for predicting student performance using Artificial Intelligence. Specifically, we propose two-step academic performance prediction using feature weighted support vector machine and artificial neural network (ANN) learning. A feature weighted SVM, where the importance of different features to the outcome is calculated using information gain ratios, is employed to perform coarse-grained binary classification (pass, P 1 , or fail, P 0 ). Subsequently, detailed score levels are divided from D to A+, and ANN learning is employed for fine-grained, multi-class training of the P 1 and P 0 classes separately. The experiments and our subsequent ablation study, which are conducted on the student datasets from two Portuguese secondary schools, have proved the effectiveness of this hybridized method. [ABSTRACT FROM AUTHOR]

Background: With the advancement in technology the amount of data generated, in almost every sphere of life, is increasing exponentially. This enormous amount of data needs new powerful tools for analysis and inference drawing. One such process is data mining which is the automated extraction of hidden, previously unknown, and useful knowledge from big data. Data mining is crucial as conventional strategies cannot keep up with the analysis of rapidly accumulating data and they are also inflexible in the wake of new challenges. Animal sciences are no exception to the changing scenario, especially when animal farms are quickly becoming more data intensive. The amount of data generated on the farms is also growing exponentially as farms become more intensive and mechanized. There is thus a need to utilize the knowledge of multidisciplinary fields like advanced statistics, artificial intelligence, machine learning, and database management, for revamping animal sciences. Artificial neural networks (ANNs) offer a lot of promise in this direction since they are motivated by the distributed, massively parallel computation in the brain. ANNs are powerful machine learning tools that offer multiple advantages for data mining over traditional techniques in being fast, accurate, self-organizing, robust, and highly accepting of noisy and imprecise data. Neural networks are being applied successfully for a myriad of supervised and unsupervised learning applications to draw useful hitherto unknown inferences, patterns, and relationships. Neural networks have been used successfully for pattern recognition, clustering, forecasting, prediction, and classification in animal sciences due to their capacity to learn from data, their nonparametric nature, and their ability to generalize well. Today ANN computing is a major element within any data mining tool kit. Popular methods used for neural network computing include feed-forward networks, feedback networks, and self-organization networks. ANN also offers powerful and distributed computing architecture, especially under a scenario where the data are readily available in significant quantity. Short conclusion: This paper gives an overview of ANN and their applications in animal sciences and reviews major research conducted in this new and exciting area of artificial intelligence. Research in many aspects of ANN in Animal Sciences has been conducted globally although there is scope for more research in aspects of animal health, monitoring, breeding as well as nutrition. [ABSTRACT FROM AUTHOR]

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper. [ABSTRACT FROM AUTHOR]

How to build a football match prediction model and use scientific methods to solve the prediction problem has become a key point in the application of artificial intelligence in the sports industry. In this paper, we choose a BP neural network model that is powerful in processing nonlinear data to perform research. According to the demand, this paper constructs a gray fuzzy prediction model based on neural network, a gray extreme learning machine prediction model, and a gray fuzzy extreme learning machine prediction combination model based on neural network. Moreover, this paper tests the neural network model by comparing actual results with predicted results. In addition, by predicting and analyzing the football league data, this article tests the three models in terms of match result prediction accuracy, data processing speed, data transmission accuracy, match analysis scores, etc., and uses statistical analysis methods to process data, and uses intuitive statistical graphs to obtain the processing results. The research results show that the gray fuzzy extreme learning machine prediction combination model based on neural network constructed in this paper can retain the advantages of a single model and effectively improve the prediction accuracy of the model and the performance of the system. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a computationally economic lightweight artificial intelligence (AI) technology for smartphones. Until date, no commercial system is available on this technology. Thus the developed breakthrough technology can enhance the capability of users on the field for monitoring the agricultural vehicles (AgV)s health by analyzing the acoustic noise using smartphone's app. This paper can enable the user of AgVs to optimize their farming by management at edge devices: smartphones. Since smartphones use a small integrated computing unit with computational limited resources, thus lighter the system, more favorable to work on. Artificial neural network (ANN) is one of the most favorite AI techniques, but its lightweight architecture—attributed by the number of inputs and hidden layers and neurons—, is one of the most important issues in the context of smartphones. Under the framework of bi-level optimization, we aim to analyze the tournament selection operator based genetic algorithm with hybrid crossover operators, at level-I, to evolve ANN, at level-II to design the lightweight edge device enabled AI technique. The obtained results and numerical evaluative analysis indicate that the optimized design of the lightweight fault detection system responds well to the soundtracks received via microphones. We present the evaluation of the proposed technology on serial programming, parallel programming (PP), GPU programming, and GPU with PP. The results show that PP is enough efficient for the proposed technology and can save the cost of GPU for large scale implementation of the technology. [ABSTRACT FROM AUTHOR]

The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting beamforming prediction using deep learning algorithms in these networks. This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. It is indicated that the initial DNN model is vulnerable to adversarial attacks, such as Fast Gradient Sign Method , Basic Iterative Method , Projected Gradient Descent , and Momentum Iterative Method , because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods defend the DNN models against adversarial attacks in next-generation wireless networks. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is emerging as a prime area of research in the modern era. The significance of IoT in the daily life is increasing due to the increase in objects or things connected to the internet. In this paper, routing protocol for low power and lossy networks (RPL) is examined on the Contiki operating system. This paper used RPL attack framework to simulate three RPL attacks, namely hello-flood, decreased-rank and increased-version. These attacks are simulated in a separate and simultaneous manner. The focus remained on the detection of these attacks through artificial neural network (ANN)-based supervised machine learning approach. The accurate detection of the malicious nodes prevents the network from the severe effects of the attack. The accuracy of the proposed model is computed with hold-out approach and tenfold cross-validation technique. The hyperparameters have been optimized through parameter tuning. The model presented in this paper detected the aforesaid attacks simultaneously as well as individually with 100% accuracy. This work also investigated other performance measures like precision, recall, F1-score and Mathews correlation coefficient (MCC). [ABSTRACT FROM AUTHOR]

Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of stakeholders of SMEs is connecting, accessing, exchanging, adding, and changing the transactional executions. The balanced lifecycle of SMEs requires partnership exchanges, financial management, manufacturing, and productivity stabilities, along with privacy and security. Interoperability platform issue is another critical challenging aspect while designing and managing a secure distributed Peer-to-Peer industrial development environment for SMEs. However, till now, it is hard to maintain operations of SMEs' integrity, transparency, reliability, provenance, availability, and trustworthiness between two different enterprises due to the current nature of centralized server-based infrastructure. This paper bridges these problems and proposes a novel and secure framework with a standardized process hierarchy/lifecycle for distributed SMEs using collaborative techniques of blockchain, the internet of things (IoT), and artificial intelligence (AI) with machine learning (ML). A blockchain with IoT-enabled permissionless network structure is designed called "B-SMEs" that provides solutions to cross-chain platforms. In this, B-SMEs address the lightweight stakeholder authentication problems as well. For that purpose, three different chain codes are deployed. It handles participating SMEs' registration, day-to-day information management and exchange between nodes, and analysis of partnership exchange-related transaction details before being preserved on the blockchain immutable storage. Whereas AI-enabled ML-based artificial neural networks are utilized, the aim is to handle and optimize day-to-day numbers of SME transactions; so that the proposed B-SMEs consume fewer resources in terms of computational power, network bandwidth, and preservation-related issues during the complete process of SMEs service deliverance. The simulation results present highlight the benefits of B-SMEs, increases the rate of ledger management and optimization while exchanging information between different chains, which is up to 17.3%, and reduces the consumption of the system's computational resources down to 9.13%. Thus, only 14.11% and 7.9% of B-SME's transactions use network bandwidth and storage capabilities compared to the current mechanism of SMEs, respectively. [ABSTRACT FROM AUTHOR]

The proposed paper presents the analysis, design, implementation and evaluation of an ultra-short-term frequency trading system for the foreign exchange (FOREX) market, which features all stages of the trading process (Pretrade Analysis, Trend Forecasting, Transaction Execution) substantially exploiting artificial intelligence techniques. Our goal is to simulate the judgment and decision making of the human expert (technical analyst or broker) with a system that responds in a timely manner to changes in market conditions, thus allowing the optimization of ultra-short-term transactions. We designed and implemented a series of technical indicator simulators, which are fed to a novel artificial neural network architecture, to eventually generate the trend forecasting signal. We also designed and implemented a series of customizable ultra-short-term automated trading machines, which receive as inputs the generated forecasting signals and perform real-time virtual transactions. A comparative analysis of the results of both automated trading machines and each machine is carried out for a comprehensive variety of trend forecasting sources. [ABSTRACT FROM AUTHOR]

The present stage of development of artificial intelligence (AI) is based on advanced technologies and modern methods and algorithms of machine learning (ML) including deep machine learning, intellectual data analysis and other fundamental scientific areas, which in turn play an important role in delivering applied solutions in almost all areas of digital economy. However, the corresponding expansion of the AI application range and complication of the class of problems, as well as of the spectrum and the volume of data used for creation of applied AI models and AI-based intelligent system call for a significant expansion of the theoretical and algorithmic basis of AI including development of ML methods that involve mathematical and physical models of objects and phenomena of domains, methods of fusion of multimodal data, methods for constructing geometric and topological components of deep neural networks, methods for modeling 3D objects, etc. In 2021, in order to cope well with these challenges, Skoltech Research Center for Applied Artificial Intelligence was created with support from the Federal Program "Artificial Intelligence." The aims of the Center are as follows: creation of a scientific and technological basis for solution of a wide range of relevant applied tasks for purposes of sustainable development of the economy of the Russian Federation including optimization problems of managerial decisions aimed at reduction of the carbon footprint and other urgent ESG tasks; environmental monitoring tasks for detection of anomalies and extreme situations forecast; assessment of economic and social risks and their dynamics caused by climatic changes; predictive analytic problems, etc. In the present paper, we describe new AI technologies, models, methods and algorithms developed in the Center, describe the main applied areas of research of the Center, and provide a briefly survey of the already developed scientific and applied results. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes. [ABSTRACT FROM AUTHOR]

Machine Translation is a mechanism of transforming text from one language to another with the help of computer technology. Earlier in 2018, a machine translation system had been developed by the authors that translate Sanskrit text to Universal Networking Language expressions and was named as SANSUNL. The work presented in this paper is an extension of SANSUNL system by enhancing POS tagging, Sanskrit language processing and parsing. A Sanskrit stemmer having 23 prefixes and 774 suffixes with grammar rules are used for stemming the Sanskrit sentence in the proposed system. Bidirectional long short-term memory (Bi-LSTM) and stacked LSTM deep neural network models have been used for part of speech tagging of the input Sanskrit text. A tagged dataset of around 400 k entries for Sanskrit have been used for training and testing the neural network models. Proposed Sanskrit context-free grammar has been used with CYK parser to perform the parsing of the input sentence. Size of the Sanskrit-Universal Word dictionary has been increased from 15000 to 25000 entries. Approximately 1500 UNL generation rules have been used to resolve the 46 UNL relations. Four datasets UC-A1, UC-A2, Spanish server gold standard dataset, and 500 Sanskrit sentences taken from the general domain have been used for validating the system. The proposed system is evaluated on BLEU and Fluency score metrics and has reported an efficiency of 95.375%. [ABSTRACT FROM AUTHOR]

The saturated hydraulic conductivity, ksat, is a crucial variable to describe the hydromechanical behavior of soils. The value of ksat of lateritic soils that are typically found in tropical regions is highly affected by the soil's structure, void ratio, and fine particle aggregation. As a result, the determination of ksat in the field or in the laboratory is complex and involves greater variability, depending on the type of test and on the spatial location of sampling. This paper presents a study of ksat values of lateritic soils, analyzing them using Statistic, Multilayer Perceptron Artificial Neural Networks (ANN) and Decision Trees (CHAID). This study aims to support decision-making regarding the type of test and depth chosen for sampling in laterite soils and understanding the factors influencing the permeability of such soils. An extensive literature review on the ksat values of lateritic soils was performed, providing data for the establishment of a database comprise of 722 registries. According to agronomic and geotechnical soil classifications, the Brazilian lateritic soils presents a "moderate" hydraulic conductivity. A significant variation of permeability values along the depth was identified, particularly for depths between 0.1 and 0.2 m. Regarding the importance of testing variables, the ANN indicated a high dependency on the type of test. The decision tree divided field test and laboratory test automatically, inferring the relevance of the type of test to the determination of ksat. [ABSTRACT FROM AUTHOR]

This paper extends previous studies work on the application of Machine Learning (ML) to distortion compensation in optical communication systems with optical fibers nonlinear coefficients (γ) that exceed current industry standards e.g. γ > 1.4 W - 1 k m - 1 . To quantify the improvement afforded by ML methods under different transmission conditions the procedures are applied to a model of a typical single-frequency optical communication system with, a 3200 km fiber length, double polarization, and a 16-QAM modulation format. The performance of both transmitters and receivers that incorporate Neural Networks (NNs) are then examined over a wide range of γ values. In all cases considered, the system Q-factor is improved although the degree of enhancement is dependent on the signal to noise ratio. The ML structures that were investigated include Siamese neural networks (SNN) implemented at the receiver end as well as two-stage architectures that employ NNs at the transmitter together with a classifier at the receiver side. Classifiers ranging from simple decision tree structures to boosting, forests, extra trees, and Multi-layer perceptron (MLP) were further examined and found to provide significant enhancement for γ > 4 W - 1 k m - 1 . The optimal performance for highly nonlinear systems was achieved for two-stage systems with random forest or extra tree classifiers. Finally, empirical equations for each ML technique were derived that relate the Q-factor enhancement and the value of γ , and the number of triplet terms that are input into the neural network. These results could potentially be employed to relax manufacturing constraints and accordingly reduce system costs. [ABSTRACT FROM AUTHOR]

The objective of this work is to compare the efficiency of three computational intelligence techniques: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to model the dynamic adsorption of organic pollutants on activated carbon. A comparison study was enhanced using five models: ANN with conventional transfer functions, ANN with new transfer function called "SPOCU", SVM, SVM hybrid with Dragonfly optimisation algorithm (SVM-DA) and ANFIS. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics of adsorption on activated carbon, was used in the modelling process. The studied parameters were molar mass, initial concentration, flow rate, bed height, BET surface area, time and concentration of non-dimensional effluents. Overall, the developed model was able to accurately estimate 11,763 experimental data points gathered from the literature. The performance of the optimised models has been evaluated using different metrics between the experimental and the predicted data. Results show that SVM-DA model can estimate accurately the dynamic adsorption of organic pollutants on activated carbon against the other tested models. Also a graphical user interface is developed in this paper in order to keep the traceability of the estimated results. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account. [ABSTRACT FROM AUTHOR]

The smarter world needs more efforts to purposeful manage and usage of technologies, science, artificial intelligence, and artificial neural networks, as their product. One of the main tools to facilitate collecting beneficial information is reviewing the publications of the target domain. To this end, the present paper attempts to categorize recent trends and innovations in the application of artificial neural networks in water quality and pollution research. The most prevalent methods for water pollution and quality prediction modeling during 2011–2020 were BPNN and normal MLP.. Conducting and comparing other analytical methods such as ANN, RBFNN, LSTM, and CNN to find accurate results are in the next orders. Moreover, along with the emerging and development of the internet of things (IoT), it was observed that IoT as a real-time technology for collecting relevant data, which appeared in the water quality analysis since 2016–2017, was used increasingly in 2019 and 2020. Furthermore, reviewed papers revealed that pH, chemical oxygen demand, and biochemical oxygen demand were the most water quality indicators. Regionally, drivers in China were the most investigated sites during the last decade, and then India and Turkey were the second and third place. [ABSTRACT FROM AUTHOR]

Introducing neuro-fuzzy concept in decision-making problems, makes a new way in artificial intelligence and expert systems. Sometimes, neural networks are used to optimize certain performances. In general, knowledge acquisition becomes difficult when problem's variables, constraints, environment, decision maker's attitude and complex behavior are encountered with. A sense of fuzziness prevails in these situations, sometimes numerically and sometimes linguistically. Neural networks (or neural nets) help to overcome this problem. Neural networks are explicitly and implicitly hyped to draw out fuzzy rules from numerical information and linguistic information. Logic-gate and switching circuit mobilize the fuzzy data in crisp environment and can be used in artificial neural network, also. Game theory has a tremendous scope in decision making; consequently, decision makers' hesitant characters play an important role in it. In this paper, a game situation is clarified under artificial neural network through logic-gate switching circuit in hesitant fuzzy environment with a suitable example, and this concept can be applied in future for real-life situations. [ABSTRACT FROM AUTHOR]

In general, the created era of a literary work is significant information for understanding the background and the literary interpretation of the work. However, in the case of literary works of old Korea, especially works created in Hangul, there are few works of which the era of creation are known. In this paper, the created era of old Korean documents was estimated based on artificial intelligence. Hangul, a Korean letter system where one syllable is one character, has more than 10,000 combinations of characters, so it is available to predict changes in the structure or grammar of Hangul by analyzing the frequency of characters. Accordingly, a deep neural network model was constructed based on the term frequency of each character in Hangul. Model training was performed based on 496 documents with known publication years, and the mean-absolute-error of the test set for the entire prediction range from 1447 to 1934 was 13.77 years for test sets and 15.8 years for validation sets, which is less than an error ratio of 3.25% compared to the total year range. In addition, the predicted results of works from which only the approximate creation time was inferred were also within the range, and the predicted creation years for other divisions of the identical novel were similar. These results show that the deep neural network model based on character term frequency predicted the creation era of old Korean documents properly. This study is expected to support the literary history of Korea within the period from 15C to 19C by predicting the period of creation or enjoyment of the work. In addition, the method and algorithm using syllable term frequency are believed to have the potential to apply in other language documents. [ABSTRACT FROM AUTHOR]

Multimedia IoT (M-IoT) is an emerging type of Internet of things (IoT) relaying multimedia data (images, videos, audio and speech, etc.). The rapid growth of M-IoT devices enables the creation of a massive volume of multimedia data with different characteristics and requirements. With the development of artificial intelligence (AI), AI-based multimedia IoT systems have been recently designed and deployed for various video-based services for contemporary daily life, like video surveillance with high definition (HD) and ultra-high definition (UHD) and mobile multimedia streaming. These new services need higher video quality in order to meet the quality of experience (QoE) required by the users. Versatile video coding (VVC) is the new video coding standard that achieves significant coding efficiency over its predecessor high-efficiency video coding (HEVC). Moreover, VVC can achieve up to 30% BD rate savings compared to HEVC. Inspired by the rapid advancements in deep learning, we propose in this paper a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Therefore, we replace the conventional in-loop filtering in VVC by the proposed WSE-DCNN model that eliminates the compression artifacts in order to improve visual quality and hence increase the end user QoE. The obtained results prove that the proposed in-loop filtering technique achieves - 2.85 %, - 8.89 %, and - 10.05 % BD rate reduction for luma and both chroma components under random access configuration. Compared to the traditional CNN-based filtering approaches, the proposed WSE-DCNN-based in-loop filtering framework achieves efficient performance in terms of RD cost. [ABSTRACT FROM AUTHOR]

In the past decade, social media networks have received much attention among ordinary people, agencies, and research scholars. Twitter is one of the fastest-growing social media tools. By means of the Twitter application on smartphones, users are able to immediately report events happening around them on a real-time basis. The information disseminated by millions of active users every day generates a new version of a dynamic database that contains information about various topics. Twitter data can be utilized as a major traffic data source along with conventional sensors. In this aspect, this paper presents a novel firefly algorithm-based feature selection with a deep learning model for traffic flow analysis (FFAFS-DLTFA) using Twitter data. The goal of FFAFS-DLTFA is to determine the class labels for tweets as relevant to traffic events. The proposed FFAFS-DLTFA encompasses several processes, such as preprocessing, feature extraction, feature selection, and classification. Primarily, tweets are preprocessed in several ways, such as tokenization, removal of stop words, and stemming. At the same time, three types of embedding vectors, unigram, bigram, and POS features, are used. In addition, the firefly algorithm (FFA) is applied for the optimal selection of feature subsets. Finally, a deep neural network (DNN) model is applied for the identification of tweets into three classes, namely, positive, neutral, and negative. The performance validation of FFAFS-DLTFA takes place using the benchmark Kaggle repository, and the results are inspected under different aspects. The experimental values demonstrate the better performance of FFAFS-DLTFA on the other techniques with the maximum accuracy of 98.83%. [ABSTRACT FROM AUTHOR]

The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn 2 O 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman's exponent and shape factor with 0.97 R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries. [ABSTRACT FROM AUTHOR]

The banking sector is on the eve of a serious transformation and the thrust behind it is artificial intelligence (AI). Novel AI applications have been already proposed to deal with challenges in the areas of credit scoring, risk assessment, client experience and portfolio management. One of the most critical challenges in the aforementioned sector is fraud detection upon streams of transactions. Recently, deep learning models have been introduced to deal with the specific problem in terms of detecting and forecasting possible fraudulent events. The aim is to estimate the unknown distribution of normal/fraudulent transactions and then to identify deviations that may indicate a potential fraud. In this paper, we elaborate on a novel multistage deep learning model that targets to efficiently manage the incoming streams of transactions and detect the fraudulent ones. We propose the use of two autoencoders to perform feature selection and learn the latent data space representation based on a nonlinear optimization model. On the delivered significant features, we subsequently apply a deep convolutional neural network to detect frauds, thus combining two different processing blocks. The adopted combination has the goal of detecting frauds over the exposed latent data representation and not over the initial data. [ABSTRACT FROM AUTHOR]

Automated continuous condition monitoring of industrial electrical machines to identify internal faults has become one of the critical research areas for the past decade. Among various defects, early-stage identification of insulation failure in stator winding is of notable demand as it often occurs and accounts for 37% of the overall motor failures. Identifying the current signature at its embryonic stage will effectively improve industrial machinery's downtime and repair costs. Recent advances in computational performance and sensor technology concede advanced systems for achieving these goals. The design of an AI-based fault signature analyzer (FSA) has been developed in this paper. FSA uses real-time stator current data in the time and frequency domain from healthy and faulty induction motors to train the various AI-based machine learning classifiers to identify health conditions using wavelets. Comparing machine learning algorithms such as artificial neural network, random forest, fuzzy logic, neuro-fuzzy logic, K-nearest neighbors is performed, and various performance attributes are quantified. A reliable, automatic fault signature from a motor current is thus analyzed using the fusion of a wavelet-based feature extraction technique and a capable knowledge-based efficient artificial intelligence (AI) approach. [ABSTRACT FROM AUTHOR]

Cement factories require large amounts of energy. 70% of the variable cost goes to energy—33% to kiln thermal energy and 37% to electrical energy. This paper represents the second stage of a broader research study which aims at optimising electricity cost in a cement factory by means of using artificial intelligence. After an analysis of the different tools that could be highly useful for the optimisation of electricity cost, for which a systematic review of the literature and surveys and an expert panel of 42 professionals in the cement sector were carried out, a methodology was developed in order to reduce electricity cost by optimising not only different variables of the production process, but also regulated electricity costs and electricity market costs. Artificial neural networks and genetic algorithms will be the tools to be used in this methodology, which can be applied to any cement plant in the world, and, by extension, to any electro-intensive consumer. The innovation of this research work is based on the use of a methodology that not only combines two different variables at the same time—process variables and regulated prices—but also makes use of artificial intelligence tools techniques. [ABSTRACT FROM AUTHOR]

This paper proposes an artificial intelligence (AI)-based new control algorithm for a self-balancing quadruped robot. A quadruped robot is a good example of a redundant degree-of-freedom (DOF) system and is designed for locomotion over extreme terrain conditions. Even though a relevant control algorithm exerts a great effect on the performance of the locomotion control of quadruped robots, controlling them is difficult and complex because of the redundant DOF and interlocked movement of their four legs. This paper presents an effective control algorithm that can replace the typical analysis-based control theory, including inverse kinematics, differential equations of motion, and governing equations, which is based on reinforcement learning (RL) and artificial neural network (ANN). RL generates the training data to train the ANN model, and the trained ANN model is finally used to control a quadruped robot. The proposed AI-based robot-control algorithm is validated experimentally using a customized test-bed and a self-balancing quadruped robot. The results show that the proposed method is a promising new control algorithm that can replace the mathematically incomprehensible robot-control system. [ABSTRACT FROM AUTHOR]

Resource Planning Optimization (RPO) is a common task that many companies need to face to get several benefits, like budget improvements and run-time analyses. However, even if it is often solved by using several software products and tools, the great success and validity of the Artificial Intelligence-based approaches, in many research fields, represent a huge opportunity to explore alternative solutions for solving optimization problems. To this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two subnetworks: the person network (PN) and the item network (IN). The PN processes the response pattern of individual respondent and generates an estimate of the underlying ability, while the IN takes in the response pattern of individual item and outputs the estimates of the item parameters. Four simulation studies and an empirical study were comprehensively and rigorously conducted to investigate the performance of CEN on parameter estimation of the two-parameter logistic model under various testing scenarios. Results showed that CEN effectively fit the training data and produced accurate estimates of both person and item parameters. The trained PN and IN adhered to AI principles and acted as intelligent agents, delivering commendable evaluations for even unseen patterns of new respondents and items. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is critical for data-driven decision making to increase resource utilization, operational performance, and service quality in various industry domains, particularly in healthcare. Using AI in healthcare operations can significantly improve treatment outcomes and enhance patient satisfaction while reducing costs. In this paper, we propose a multi-stage framework to build an AI-based decision support tool that can predict the 5-year survivability of lung cancer patients. We evaluate the proposed framework using the Surveillance, Epidemiology, and End Results dataset pertaining to the 1973–2015 period obtained from the National Institutes of Health. The first stage entails data preprocessing and target creation. The second stage applies six AI algorithms with feature selection through Particle Swarm Optimization and hyperparameter tuning with Cross-Validation. These Algorithms include Logistic Regression, Decision Trees, Random Forests (RF), Adaptive Boosting (AdaBoost), Artificial Neural Network, and Naïve Bayes. The results show that RF and AdaBoost models yield an AUC rate of 0.94 and outperform the other models. Stage 3 utilizes permutation importance to interpret the RF and AdaBoost models and applies Tree-based Augmented Naïve Bayes to gain insights regarding the interrelations among important features. The results of Stage 3 delineate that the number of lymph nodes containing metastases), the number of tumors that patients have had in their lifetime, the patient's age, and the microscopic composition of cells rank among the topmost important features and can significantly impact patient survivability. We think this study has significant practical implications in helping physicians predict prognosis and develop treatment plans for lung cancer patients. [ABSTRACT FROM AUTHOR]

Experience shows that cooperating and communicating computing systems, comprising segregated single processors, have severe performance limitations, which cannot be explained using von Neumann's classic computing paradigm. In his classic "First Draft," he warned that using a "too fast processor" vitiates his simple "procedure" (but not his computing model!); furthermore, that using the classic computing paradigm for imitating neuronal operations is unsound. Amdahl added that large machines, comprising many processors, have an inherent disadvantage. Given that artificial neural network's (ANN's) components are heavily communicating with each other, they are built from a large number of components designed/fabricated for use in conventional computing, furthermore they attempt to mimic biological operation using improper technological solutions, and their achievable payload computing performance is conceptually modest. The type of workload that artificial intelligence-based systems generate leads to an exceptionally low payload computational performance, and their design/technology limits their size to just above the "toy" level systems: The scaling of processor-based ANN systems is strongly nonlinear. Given the proliferation and growing size of ANN systems, we suggest ideas to estimate in advance the efficiency of the device or application. The wealth of ANN implementations and the proprietary technical data do not enable more. Through analyzing published measurements, we provide evidence that the role of data transfer time drastically influences both ANNs performance and feasibility. It is discussed how some major theoretical limiting factors, ANN's layer structure and their methods of technical implementation of communication affect their efficiency. The paper starts from von Neumann's original model, without neglecting the transfer time apart from processing time, and derives an appropriate interpretation and handling for Amdahl's law. It shows that, in that interpretation, Amdahl's law correctly describes ANNs. [ABSTRACT FROM AUTHOR]

Cold metal transfer (CMT) welding is a sophisticated version of fusion welding process available with advanced features incorporated in it. During the process of joining metallic parts with CMT, the bead shape and size have significant effects on the weld quality. Typically, bead geometry is characterized by the three important parameters namely weld width, weld depth and reinforcement height. For rapid and intelligent welding, it is imperative to monitor the bead shape and size during the process. However, the measurement system of three parameters is often time consuming and complex. In this paper, an attempt is made to reduce the computational time of artificial intelligence models that are used for intelligent welding processes. Bead coefficient is modelled as an alternative for bead geometry properties. Computationally efficient artificial neural network models are developed for examining the feasibility of bead coefficient over bead geometry properties with and without online-temperature measurements. The predicted results of both forward and reverse neural network models are in good coherence with the experimental values. The outcome of this study is expected to provide a detailed understanding of effects of process parameters on bead geometry and bead coefficient, which facilitates online monitoring of welding processes. [ABSTRACT FROM AUTHOR]