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Digital systems storing cadastral data in vector format are considered effective due to their ability of offering interactive services to citizens and other land-related systems. The adoption of such systems is ubiquitous, but when adopted, they create two non-compatible systems with paper-based cadastral systems whose information needs to be digitised. This study proposes a new approach that is fast and accurate for transforming paper-based cadastral data into digital systems. The proposed method involves deep-learning techniques of the LCNN and ResNet-50 for detecting cadastral parcels and their numbers, respectively, from the cadastral plans. It also contains four functions defined to speed up transformations and compilations of the cadastral plan's data in digital systems. The LCNN is trained and validated with 968 samples. The ResNet-50 is trained and validated with 106,000 samples. The Structural-Average-Precision ( sAP 10 ) achieved with the LCNN was 0.9057. The Precision, Recall and F1-Score achieved with the ResNet-50 were 0.9650, 0.9648 and 0.9649, respectively. These results confirmed that the new method is accurate enough for implementation, and we tested it with a huge set of data from Tanzania. Its performance from the experimented data shows that the proposed method could effectively transform paper-based cadastral data into digital systems. [ABSTRACT FROM AUTHOR]

Drying of sewage sludge is typically modeled as simultaneous heat and mass transfer phenomena. The capability of conventional models to take into account crust formation, cracks, and shrinking is limited. Artificial neural networks (ANNs) are suitable tools for dynamic representation of drying processes; however, obtaining a suitable database is a resource consuming task. Based on the Taguchi method, nine experiments were defined to set up a training database and to develop an ANN model. A high Pearson correlation coefficient was verified when comparing the drying kinetic curve generated by the ANN model with the one obtained during the validation experiments. [ABSTRACT FROM PUBLISHER]

The components include E-shape single element slot antenna, Wilkinson Power Divider, Hybrid Directional Coupler, Loaded Line Phase Shifter, and Switched Line Phase Shifter. The following paper on "An Optimized DTW Algorithm using RMSE Approach to Classify the Liquids in Ka-Band", presents an algorithm to classify liquids with high level accuracy using transmission parameter of liquids with microwave spectroscopy. The authors study four types of textile antennas with wash cotton and denim substrate and compare the performance with standard substrate antenna for circular polarization, gain and return loss. The algorithm uses Dissolved Gas Analysis samples from real power transformers and genetic algorithm based fuzzy classifier. [Extracted from the article]

Guizzaro et al. ([2]) evaluated the performance of different estimation techniques in trials with incomplete follow-up after discontinuation when a treatment-policy strategy is implemented. They provided several guidance on the estimation of an estimand that employs a treatment-policy strategy for treatment discontinuation. [Extracted from the article]

Although flexible neural networks (FNNs) have been used more successfully than classical neural networks (CNNs) in many industrial applications, nothing is rigorously known about their properties. In fact they are not even well known to the systems and control community. In the first part of this paper, existing structures of and results on FNNs are surveyed. In the second part FNNs are examined in a theoretical framework. As a result, theoretical evidence is given for the superiority of FNNs over CNNs and further properties of the former are developed. More precisely, several fundamental properties of feedforward and recurrent FNNs are established. This includes the universal approximation capability, minimality, controllability, observability, and identifiability. In the broad sense, the results of this paper help that general use of FNNs in systems and control theory and applications be based on firm theoretical foundations. Theoretical analysis and synthesis of FNN-based systems thus become possible. The paper is concluded by a collection of topics for future work. [ABSTRACT FROM PUBLISHER]

In total, more than 200 papers were reviewed and classified into 4 categories which are: machine learning assisted VRP modelling, machine learning guided VRP decomposition strategies, machine learning guided perturbative VRP algorithms, and finally learning to construct VRP solutions. It provides an extensive review of vehicle routing (VRP) researches that use both analytical optimisation approaches and machine learning (ML) modules and mechanisms. This special issue largely originated from various discussions during several cross-domain, multi-disciplinary conferences and workshops, especially the 9th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA2019), which attracted scientists, researchers and practitioners from Computer Science, Operations Research as well as Business and Management. " A Two-Stage Stochastic Programming Model for Collaborative Asset Protection Routing Problem Enhanced with Machine Learning; A Learning Based Matheuristic Algorithm.". [Extracted from the article]

Although breast cancer (BC) deaths have decreased over time, it remains the second leading cause of cancer-related deaths among women. With the technical advancement of artificial intelligence (AI) and availability of healthcare data, many researchers have attempted to employ machine learning (ML) techniques to gain a better understanding of this disease. The present study was a systematic literature review (SLR) of the use of machine learning techniques in breast cancer screening (BCS) between 2011 and 2021. A total of 66 papers were selected and analysed to address nine criteria: year of publication, publication venue, paper type, BCS modality, empirical type, ML technique, performance, advantages and disadvantages, and dataset used. The results showed that mammography was the most frequently used BCS modality, and that classification was the most used ML objective. Moreover, of the six investigated ML techniques, convolutional neural network models scored the highest median accuracy with 96.67%, followed by adaptive boosting (88.9%). [ABSTRACT FROM AUTHOR]

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator [ABSTRACT FROM AUTHOR]

The authors of the paper titled "Towards Neuro-Fuzzy Compensated PID Control of Lower Extremity Exoskeleton System for Passive Gait Rehabilitation" designed a neuro-fuzzy compensated PID control for passive gait rehabilitation using a lower extremity exoskeleton system. In the paper "A Comparative Survey of Convex Combination of Adaptive Filters", the authors evaluate the convex combination of adaptive filters, which are mostly based on the least mean square (LMS) algorithm, the affine projection (AP) algorithm, and the recursive least mean square (RLS) algorithm. The paper "A Novel Weighted Superposition Attraction Algorithm-based Optimization Approach for State of Charge and Power Management of an Islanded System with Battery and Super Capacitor-based Hybrid Energy Storage System" proposes a Hybrid Energy Storage System (HESS) employing a Battery and a Super Capacitor (SC). The authors of the paper titled "Computer-Aided Detection and Diagnosis of Thyroid Nodules Using Machine and Deep Learning Classification Algorithms" propose a computer-assisted method for detecting and segmenting tumour regions in ultrasound thyroid images using machine and deep learning classification algorithms. [Extracted from the article]

In this paper, the author introduced a sentiment analysis-based customer loyalty prediction model in mobile applications using word embedding models, deep learning algorithms, and deep contextualized word representations. This method showed better performances than usual state-of-the-art methods in separate data sets along with the combined data set on both gender and region classification. Intelligent Systems can be thought of as a concept with a very broad scope. [Extracted from the article]

The second paper, titled "Design and Analysis of Zeta Converter for Power Factor Correction using Cascade PSO-GSA Tuned PI and Reduced-Order SMC" describes a single-phase power factor correction method employing a DCDC Zeta converter in continuous conduction mode.To increase converter performance, it presents a non-linear cascade control of particle swarm optimization-gravitational search algorithm (PSO-GSA) tuned proportional-integral and sliding mode controller in outer and inner loops. In particular, an optimisation framework is developed that accounts for several processes, including data gathering from various sources, KPI description, and the optimisation procedure itself. Wind farm plan optimization or WFLOP, is a difficult optimisation problem with many constraints. Heuristic optimisation techniques outperformed standard methods in the study. [Extracted from the article]

The research reviews conducted by Lo and Chai ([2012]. Quantitative analysis of quality management literature published in total quality management and business excellence (1996–2010). Total Quality Management & Business Excellence, 23(5-6), 629–651. doi:10.1080/14783363.2012.669553) and Dereli et al. ([2011]. An analysis of the papers published in total quality management & business excellence from 1995 through 2008. Total Quality Management & Business Excellence, 22(3), 373–386. doi:10.1080/14783363.2010.532337) were based on TQM&BE, and together they covered the period between 1995 and 2010. The limitation of these two studies is that the study period ended in 2010. Accordingly, this study extends those previous studies by reviewing papers published between 2010 and 2019. TQM&BE is a suitable object for analysis because it is one of the top journals cited in the quality management field and plays a key role in the quality and scientific knowledge of TQM. This study demonstrated the feasibility of using the (self-organizing map) SOM algorithm to build a topic map. The SOM technique is one of the most popular artificial neural networks (ANNs). Based on this map, six topic areas were identified. In addition, the new identified themes after 2010 include the BE framework, and innovation, learning, team, and design. The results also showed that research on TQM&BE has undergone a shift in focus from a dominance of TQM to service quality, (International Organization for Standardization) ISO, and innovation and learning. Finally, the results showed that the SOM can be considered as a technique for developing topic maps for academic research reviews. [ABSTRACT FROM AUTHOR]

The first seven vertebrae of the spine are referred to as the cervical spine. X-rays, Computed Tomography scans, Magnetic Resonance Imaging, and physical examinations methods are used to identify spine fractures which are complex and non-interpretable. To overcome these drawbacks, automated systems are required. In this paper, the fifth version of You Only Look Once (YOLO) and deep neural network have combined to detect dislocations in vertebral columns. YOLO v5 is used to detect major and minute fractures of C1 to C7 vertebrae and deep neural network is used to classify normal and fractured vertebrate. Training dataset provided by ASNR and ASSR spine radiology specialists is used to train model. Experimental results show that the proposed model offers classification accuracy of 97.7% and 89% on training and validation respectively. The proposed system is able to show exact location of fracture in Cervical Spine and perform better than existing systems. [ABSTRACT FROM AUTHOR]

In multi-antenna systems, advanced techniques such as massive multiple-input multiple-output (MIMO), beamforming, and beam selection depend heavily on the accurate acquisition of the channel state. However, pilot contamination (PC) can be a major source of interference which degrades they are performance. Moreover, the severity of PC increases as more pilots are reused between users in the wireless systems. Researchers have shown that PC can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing PC, the examination that identifies the applications and factors that distinguish these DL approaches is still limited. This paper reviews these DL approaches and the improvements needed to enhance their performance. Simulation results confirm that DL networks that learn to predict the channels directly have superior performance under PC. [ABSTRACT FROM AUTHOR]

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]

We shall live with artificial intelligence (AI). Machine learning based on the deep neural networks, supported by the exponentially growing data aggregates stored and processed in the ever expanding cloud, that is being built the ever more powerful general-purpose and specialized AI chips, places AI ever more closely alongside us humans. We will need to further investigate redesigning our individual and collaborative work in seeking increased productivity from the human-AI collaboration. How will the other team participants perceive the work-oriented attributes of the AI agents?. [Extracted from the article]

The PI control structure is based on neural network models wherein real power is controlled by an artificial neural network model and reactive power is controlled by a recurrent neural network model. The design was fabricated, and its performance study is compared with simulation study and is reported in the paper. The VSWR performance, peak antenna gain and radiation patterns are presented as function of various antenna parameters from simulation studies and measurements from fabricated antenna. [Extracted from the article]

The paper by Angelo Caregnato-Neto et al. presents a method for coordinating multiple differential-drive robots using MPC. Motion planning has been an active field of robotics research for decades. Today, the application of motion planning and model predictive control techniques has spread to various kinds of robots including legged and humanoid robots, autonomous ground vehicles, unmanned aerial vehicles, and many others. [Extracted from the article]

The papers are grouped into three categories: AI methods for manufacturing systems, AI developments specifically in semiconductor manufacturing, and AI in additive manufacturing and maintenance. They combine a deep neural network model and Markov decision processes (MDP) to rapidly generate near optimal dynamic control policies for problems that are too large to be only solved by MDP, thus showing the potential of machine learning in controlling unreliable manufacturing systems. [Extracted from the article]

In this paper, the economic optimisation of cold storage is studied. A modern cold room is mainly composed of compressors, a tank to store heat-transfer fluid and cold rooms. The main cost is incurred by energy consumption and maintenance. The price of electricity, which is known in advance, varies during the day. Production schedules that entail higher risks of compressor wear, and thus high maintenance costs, have to be avoided. The temperature inside the cold rooms must be maintained within the allowed range, and complex thermodynamic processes make it difficult to predict temperature. The tank has a limited capacity. This paper presents the first model optimising the management cost of a cold store with a tank. Maintenance costs are considered for compressors, and Artificial Neural Networks are used to forecast the temperatures in the cold rooms. An optimal Dynamic Programme is designed for the case with one cold room and a matheuristic algorithm is presented for the general case with several cold rooms. A comparison with a classical hysteresis controller shows significant savings. The impact of storage capacity on operating costs is evaluated, after which the influence of the maintenance cost value is discussed. [ABSTRACT FROM AUTHOR]

Multi-resource constrained dynamic workshop scheduling is a complex and challenging task in discrete manufacturing. In this paper, to obtain a high-performance scheduling in limited time, this problem is modelled into a Markov decision process, and solved by proximal policy optimisation algorithm, which can learn from the simulated workshop environment directly. A multi-modal hybrid neural network is used in the model to make good use of numerical state features representing workshop environment information and graphical state features representing constraint information during the learning process. Multi-label technique is used in this paper to decouple the output acts of jobs, machines, tools, and workers. Action mask technique coding the constraints is also used to prune invalid exploration. The experimental results show that compared with heuristic rules such as weighted shortest processing time, weighted modified due date, weighted cost over time, apparent tardiness cost and other reinforcement learning methods such as DeepRM and DeepRM2, the performance of the proposed method is at least 1.138 % better in scheduling penalty. [ABSTRACT FROM AUTHOR]

The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into the performance of radial distribution systems (DS) with the installation of series capacitors in feeder sections. While the use of shunt capacitors to enhance voltage profile in DS has been acknowledged, the benefits of series capacitors have only been explored to a limited extent. Load flow analysis, while commonly used for offline evaluation of DS, can at times yield inconclusive results due to convergence issues. To mitigate this, an alternative approach is proposed using Artificial Neural Networks (ANN) to directly determine system performance. The viability of the proposed technique is evaluated for IEEE 12 and IEEE 33 bus DS's having series capacitor in feeder section for voltage improvement and line loss minimization. Therefore, this paper serves to contributes to the two main novelties namely, (1) Development of efficient ANN model to directly determine the maximum voltage regulation and line losses and (2) improvement of voltage and line losses reduction through series capacitor installation in feeder section of the DS. The results show that the utilization of an ANN model enhances accuracy and speeds up predictions, while the installation of series capacitors in feeder sections improves voltage regulation and reduces line losses. [ABSTRACT FROM AUTHOR]

The stability of Wireless Sensor Networks (WSNs) is a crucial requirement in real-time applications such as military, defense, and other surveillance systems. Clustering in WSNs is one of the most predominant techniques, offering benefits such as minimizing communication time, optimizing energy utilization, lengthening the network lifespan, and ensuring network stability. Moreover, achieving the balance between energy consumption and maintaining network stability is significantly influenced by the cluster size. This research paper addresses the challenges associated with clustered-based routing and cluster formation paradigm, introducing a novel algorithm employing the principle of Artificial Neural Networks (ANN). In particular, the proposed algorithm integrates a hybrid strategy of Self Organizing Map (SOM) and K-mean clustering to form energy-efficient clusters, even ensuring the energy distribution evenly among them. The validity of the proposed algorithm has been confirmed through experimental analysis conducted using MATLAB. The simulation results demonstrate that the hybrid combination of SOM and K-means clustering is highly efficient in minimizing energy consumption and maintaining network stability. The findings also reveal an average of 80% stable network lifetime and achieve a packet reception ratio (PRR) of 98% which is much higher than two other protocols i.e. LEACH and MODLEACH protocols. [ABSTRACT FROM AUTHOR]

Driving behavior monitoring by smartphone sensors is one of the most investigated approaches to ameliorate road safety. Various methods are adopted in the literature; however, to the best of our knowledge, their robustness to the prediction of new unseen data from different drivers and road conditions is not explored. In this paper, a two-phase Machine Learning (ML) method with taking advantage of high-pass, low-pass, and wavelet filters is developed to detect driving brakes and turns. In the first phase, accelerometer and gyroscope filtered time series are fed into Random Forest and Artificial Neural Network classifiers, and the suspicious intervals are extracted by a high recall. Following that, in the next phase, statistical features calculated based on the obtained intervals are used to determine the false and true positive events. To compare the predicted and real labels of the recorded events and calculate the accuracy, a method that covers the limitations of previous sliding windows is also employed. Real-world experimental result shows that the proposed method can predict new unseen datasets with average F1-scores of 71% in brake detection and 82% in turn detection which is comparable with previous works. Moreover, by sensitivity analysis of our proposed model, it is proven that implementing high-pass and low-pass filters can affect the accuracy for turn detection up to 30%. [ABSTRACT FROM AUTHOR]

In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven performances on different population segments has highlighted the representativeness issues induced by a naive aggregation of the datasets. In this paper, we show how biasing models can remedy these problems. Based on the (approximate) knowledge of the biasing mechanisms at work, our approach consists in reweighting the observations, so as to form a nearly debiased estimator of the target distribution. One key condition is that the supports of the biased distributions must partly overlap, and cover the support of the target distribution. In order to meet this requirement in practice, we propose to use a low dimensional image representation, shared across the image databases. Finally, we provide numerical experiments highlighting the relevance of our approach. [ABSTRACT FROM AUTHOR]

Addressing the challenges of scalable and cost-effective energy performance analysis in mid to high-rise office buildings, this paper introduces a novel approach utilizing an inverse-based artificial neural network (ANN). This ANN was trained on synthetically generated heating and cooling load parameters – derived from simulations conducted in EnergyPlus – to predict characterization parameters, including the building envelope, internal heat gains, and HVAC operational parameters. Diverging from traditional forward surrogate models, this inverse surrogate model fills a critical gap in current building energy modeling approaches that are hindered by data and resource limitations. Its effectiveness is verified with a testing dataset of 3000 buildings and is further demonstrated through a case study in Ottawa, Ontario. Proving to be an efficient, cost-effective tool for energy retrofit screening, the model is enhanced by a user-friendly web-based application (Ferreira and Gunay), marking a significant advancement in accessible building energy analysis. [ABSTRACT FROM AUTHOR]

A deep learning artificial neural network model developed in Tensorflow using the ReLU function is presented in this paper to determine the effect of nine different input parameters on the mechanical strength of self-compacting geopolymer concrete (SGC). With the application of the above model, it has been observed that the convergence outcome of our model is predicted with minimum error. The novelty of our model is the ReLU function which improves the accuracy of the results. Our study presents a systematic investigation of the compressive strength, flexural strength, and tensile strengths of SGC depending upon nine vital parameters, such as GFS, Fly ash, NaOH, Na2SiO3 content, Age of specimens, Slump Value, T50, L Box and J Ring value as input. A large number of experiments were conducted to prepare a comprehensive database of the mechanical strength of Flyash-GFS-based SGC. The database has a total of 396 samples which were collected from 132 distinct mixtures, where the minimum and maximum compressive strength value is 7.7 Mpa and 66.7 Mpa, respectively. For the prediction based on deep learning, authors have performed an adequate number of iterations for both training and testing using error estimation parameters such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The error parameter values for the compressive strength of SGC are 2.42, 2.94 and 1.07, respectively, for MAE, RMSE and MSE. The other mechanical properties and their error parameter have also been thoroughly evaluated in our work. [ABSTRACT FROM AUTHOR]

In this paper, an attempt is made to extend a nonlinear two-dimensional model for large oscillation and chaotic characteristics of the annular plate coupled with a piezoelectric patch subject to external excitation. For improving the mechanical properties, the core of the electrically structure is reinforced with graphene nanoplatelets. Von-Karman nonlinear theory and Hamilton's principle are taken into consideration for the exact derivation of the general nonlinear governing equations and boundary conditions of the axisymmetric annular plate coupled with a piezoelectric patch. For developing a precise solution approach, the generalized differential quadrature element method, and Multiple scales technique, are eventually used. The results demonstrate that radius ratio, applied voltage, thickness to radius ratio, and harmonic load have an important role in the nonlinear large oscillation, and chaotic motion of the graphene platelets reinforced composite (GPLRC) annular plate coupled with a piezoelectric patch. The golden and fundamental result of the current research is that, as the thickness of the piezoelectric patch increases, the system's nonlinear frequency decreases, and the area of instability in responses and maximum amplitude or the peak point of the backbone curve of the smart annular plate increases. [ABSTRACT FROM AUTHOR]

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders. [ABSTRACT FROM AUTHOR]

With the advancement in modulation formats, optical transmission systems are becoming more adaptable and dynamic. Predicting bit errors of higher-order modulation formats is a challenging problem for the optimum design of communication systems. The existing simulation methods have persisted by employing iterative procedures that frequently incur high computing expenses and time consumption. On the other hand, deep learning (DL) algorithms have demonstrated remarkable efficacy as practical computing tools, presenting a viable approach to accelerate modulation simulations. In this paper, an artificial neural network (ANN) based bit error rate (BER) estimation scheme is proposed for the popular modulation forms including 56 Gbps 16-quadrature amplitude modulation (16QAM), 100 Gbps 32QAM, and 120 Gbps 64QAM optical system. Based on constellation diagrams (CDs) acquired with different launch power, laser linewidth, transmission distance, and OSNR, amplitude histograms (AHs) are generated via a manual preprocessing method. The properly trained ANN architecture exhibits a computational speed that surpasses traditional simulation methods by a factor exceeding 347. Moreover, the design considerations including the number of layers, nodes, activation functions, learning rate, optimizers, and evolution epochs are also investigated in detail. This research paves the way for optical transmission systems to use fast DL-based optimization strategies. [ABSTRACT FROM AUTHOR]

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

However, in the times of the 1990s, it represented a breakthrough progress or turning point for modeling of textile materials and textile processes. The paper that I would nominate that has widely impacted the following work on statistical analysis and data learning-based modeling in the textile research communities since its publication 27 years ago is 'The Prediction of Yarn Tensile Properties by Using Artificial Neural Networks', by M. C. Ramesh, R. Rajamanickam & S. Jayaraman. The basic idea of this paper has been further generalized for linking experimental data to process and material modelling, and extensively used in other textile processes beyond yarn tensile property prediction. [Extracted from the article]

In view of the emerging spread of AI in computing systems of everyday use, it is becoming apparent that AI and HCI can mutually benefit from each other in fruitful cooperation. Overall, the article provides guidance for dealing with human-AI interaction during the decision-making process and sheds light on how explainable AI can increase users' acceptance of such systems. Explainable AI: The effect of contradictory decisions and explanations on users' acceptance of AI systems. The discussion around the rapid evolution and omnipresence of Artificial Intelligence (AI) is already rich, and AI is expected to be integrated into most aspects of everyday life. [Extracted from the article]

Image based-corrosion detection has become a widespread practice for steel structures, but fine-tuning their model parameters is time-consuming. Alternatively, convolutional neural networks (CNNs) can also be trained fast and automatically, but they demand a huge training dataset. In this paper, a corrosion detection approach based on an artificial neural network (ANN) whose training dataset size is less than 0.1% of that of typical CNNs is introduced. The input layer of the proposed ANN consists of textural and color properties. In the present work, different color spaces and textural properties are examined for their impact on the robustness of the ANN. Results reveal that the best color channels can be achieved by combining CIE L*u*v* and YUV color spaces. Moreover, energy is selected as the best texture feature with respect to the ANN robustness. The proposed ANN outperforms an available image processing algorithm from the perspective of both speed and accuracy. In conclusion, this ANN can be used for actual applications after a fast and straightforward training step. [ABSTRACT FROM AUTHOR]

For bonded Fibre Reinforced Polymer (FRP) strengthening systems in civil engineering projects, the adhesive joint performance is a key factor in the effectiveness of the strengthening; however, it is known that the material properties of structural epoxy adhesives change with temperature. This present paper examines the implied relationship between the curing regimes and the storage modulus response of the adhesive using a Machine Learning (ML) approach. A dataset containing 157 experimental data collected from the scientific papers and academic theses was used for training and testing an Artificial Neural Network (ANN) model. The sensitivity analysis reveals that the curing conditions have a significant effect on the glass transition temperatures (Tg) of the adhesive, and consequently on the storage modulus response at elevated temperatures. Curing at an extremely high temperature for a long time does not, however, guarantee a better thermal performance. For the studied adhesive, curing in a warm (≥ 45°C) and dry (near 0 % RH) environment for 21 days is recommended for practical applications. A software with a Graphical User Interface (GUI) was established, which can predict the storage modulus response of the adhesive, plot the corresponding response curve, and estimate the optimum curing condition. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) management has become a highly challenging issue for many countries at different levels of development since the growth of population and urbanisation has resulted in a large increase in MSW generation. It is difficult to forecast the solid waste generated and its characteristics due to the non-linear nature of the MSW system, hence the importance of introducing artificial intelligence (AI) techniques and machine learning (ML) methods. This paper discusses a systematic literature review (SLR) on the application of AI techniques in MSW management, including waste generation prediction, waste collection and transportation and waste treatment and final disposal. The study reviewed and analysed the research studies published between 2000 and 2021, and investigated the current challenges faced by researchers in implementing AI approaches in the MSW system. It was concluded that artificial neural networks are the most used approach in various MSW-related problems. However, the lack and reliability of data are limiting the advancement of AI techniques in this field. Additionally, most studies claimed that their results are accurate and can be implemented in real-life scenarios, with an absence of a clear baseline to assess the performance of the adopted approaches. The detailed gaps and future suggestions for AI techniques in MSW systems are also discussed for further research. [ABSTRACT FROM AUTHOR]

In this paper, a machine-learning-based location prediction model for maritime transport applications is proposed. In the proposed model, only environmental parameters are used as the input of the model, which makes the model autonomous. Meaning that the proposed model does not require information from any source other than onboard sensors. The Persian Gulf is used as the test case in this study. The sea depth, magnetic field intensity, and gravitational field intensity are three input features of the proposed model, and the corresponding coordination are the output. In the proposed methodology, the problem of location prediction is formulated as a regression problem and the above three parameters are used to train the main regression models. In this paper, two methods are used as the baseline, artificial neural networks (ANN) and support vector regression. The simulation results illustrate the high accuracy and good performance of the ANN in location prediction tasks. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage stochastic mathematical model is developed for an asset protection routing problem under a wildfire. The main aim of this study is to reduce the negative impact of a wildfire. Some parameters, such as travel and service times, obtaining profit by protecting an asset, and upper bounds of time windows, are considered as stochastic parameters. Generating proper scenarios for uncertain parameters has a large impact on the accuracy of the obtained solutions. Therefore, artificial neural networks are employed to extract possible scenarios according to previous actual wildfire events. The problem cannot be solved by exact solvers for large instances, so two matheuristic algorithms are proposed in this study to solve the problem in a reasonable time. In the first algorithm, a set of feasible routes is generated based on a heuristic approach, then a route-based mathematical model is used to obtain the final solution. Also, another matheuristic algorithm based on adaptive large neighbourhood search (ALNS) is proposed. In this algorithm, routing decisions are determined using the ALNS algorithm while other decisions are achieved by solving an intermediate mathematical model. The numerical analysis confirms the efficiency of both proposed algorithms; however, the first algorithm performs more efficiently. [ABSTRACT FROM AUTHOR]

Taking backorders on products is a common scenario in inventory and supply chain management systems. The ability to predict the likelihood of backorders can surely minimise a company's losses. Because the number of backorders is much lower than the number of orders that ship on time, applying a predictive model for this domain is a challenging task. This paper proposes a model that uses a deep neural network to predict backorders; it handles the data imbalance between backorders and filled orders with efficient techniques. To make the dataset balanced, we employ different techniques that include minority class weight boosting, randomised oversampling, SMOTE oversampling, and a combination of oversampling and undersampling. The balanced training data are used in our proposed, fully connected deep neural networks model to train the predictive model. The predictive model learns the likelihood of product backorders by using the training samples. We conduct experiments on a large benchmark dataset to test the performance of our proposed deep neural network–based model. The experimental results achieve a new state-of-the-art performance and outperform some prominent classification models in terms of standard evaluation metrics and expected profit measure. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies. The strengths and weaknesses of various AI methodologies are examined Artificial neural networks are extensively used for modeling drying phenomena Machine learning models can simulate complex processes of food drying Deep learning has significant potential for real-time monitoring of drying Intelligent control systems can optimize food drying [ABSTRACT FROM AUTHOR]

Point cloud registration is the process of aligning and merging multiple point clouds into a same coordinate system. It has many applications in computer vision, robotics, 3D reconstruction, augmented reality, and autonomous driving. Despite its wide-ranging utility, traditional algorithms grapple with challenges such as substantial noise, outliers, limited overlap, motion distortions, and handling large scenes. These complexities impede the effective deployment of point cloud registration in practical real-world scenarios. This paper conducts a comprehensive inquiry into the domain of deep learning-based point cloud registration. It synthesizes recent advancements in algorithms about point cloud registration grounded in deep learning. The exposition delineates insights from four distinct perspectives: multi-layer perceptron, deep neural network, graph convolutional network, and attention mechanism. Furthermore, the paper systematically reviews prevalent datasets and evaluation metrics associated with deep learning-based point cloud registration. A discussion on the existing challenges in point cloud registration and a forward-looking exploration of potential research directions are included. [ABSTRACT FROM AUTHOR]

Due to the semi-enclosed space of underground coal seams and the implementation of nitrogen injection measures, most coalfield fires occur in multi-atmosphere environments. At present, there is little research on the combustion situations and kinetics of coal under multi-atmosphere conditions. In this paper, simultaneous thermal analysis experiments were conducted to investigate the combustion behaviors of bituminous coal and anthracite in O2-CO2-N2 atmospheres with different volume fractions. A new index for quantitative characterization of coal combustion situation in multi-gas mixing atmospheres was proposed based on the combustion situation intensity index and heat release rate. Furthermore, a method to predict the coal combustion situation using artificial neural network (ANN) was proposed. The results showed that the combustion rate decreased with the increase in CO2 volume fraction and the decrease of N2 volume fraction under multi-gas mixing atmospheres. The volume fraction changes in inert gases had little effect on the ignition temperature or burnout temperature and had a greater effect on the combustion reactivity of bituminous coal. In a 15% O2-5% CO2-80% N2 atmosphere, the combustion could be inhibited for bituminous coal, while being promoted by anthracite. The combustion situation of bituminous coal in a 15% O2-25% CO2-60% N2 atmosphere was greater than that in air. Based on the proposed coal combustion situation index and the selected optimal ANN model, the coal combustion situation in O2-CO2-N2 atmospheres could be well predicted. This paper provides a reliable theoretical basis for judging the coal combustion spread in the coalfield fire area. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) based surrogate models have been widely used in place of high-fidelity simulation tools. Error metrics such as mean absolute error, and root mean squared error (RMSE) have been widely used as de facto criteria. However, whether the ANN-based surrogate model can adequately reproduce the interwoven relationships and nonlinear causalities between design variables and simulated outputs are often overlooked. In this regard, the authors designed a case study regarding four ANN-based surrogate models. It was found that despite all of the models having low RMSEs, the models failed to adequately predict the causal relationships between input variables and energy use. In other words, the surrogate models were not always capable of providing accurate assessments of expected energy use reduction as a result of design changes. In this paper, we present a workflow for validating whether a surrogate model can reproduce the causal relationships between inputs and outputs. [ABSTRACT FROM AUTHOR]

In the paper, the exponential synchronisation for delayed Clifford-valued coupled switched neural networks via static event-triggering rule is studied. Firstly, the drive-response systems for delayed Clifford-valued coupled neural network models are established. So as to avoid the non-commutativity issue of Clifford number multiplication, the original n-dimensional Clifford-valued models are decomposed into $ 2 ^mn $ 2 m n -dimensional real-valued models. On this basis, the error dynamics system is constructed, and then some new sufficient conditions are presented of the exponential synchronisation for the considered neural network models by using Lyapunov–Krasovskii (L-K) functional approach and the technique of linear matrix inequality. Finally, the effectiveness of the results are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Health information system (HIS) plays a significant and supportive role in improving the quality and efficiency of healthcare organizations and their services. The purpose of this paper is to predict the effectiveness of the health information system in terms of nurses' satisfaction using an artificial neural model. To do so, the study focuses on predicting the nurses' satisfaction with health information systems in the Gaza strip based on the task technology-fit model (TTF). A total of 164 nurses participated in the questionnaire survey from three hospitals in the Gaza strip to build the prediction dataset of the five task technology-fit factors including task characteristics, technology characteristics, attitude and task-technology fit, and nurses' satisfaction. After comparing the performance of the neural and regression models, it was found that the neural network is better than the regression model in predicting the nurses' satisfaction as the results indicated that the performance metrics including accuracy, precision, and recall of the neural model are 92.68%, 95.24%, and 90.90%; respectively. The findings will help the decision-makers and hospital managers to enhance the health information systems and nurses' satisfaction. [ABSTRACT FROM AUTHOR]

The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]