Machine Intelligent Systems Research Articles

Hybrid random projection technique for enhanced representation in high-dimensional data

This paper introduces a novel hybrid random projection method (HRP) that effectively combines the strengths of normal random projection (NRP) and plus-minus one random projection (PMRP). By incorporating a blending parameter, HRP optimises the contributions of the NRP and PMRP, reducing the dimensions of the data while ensuring that the structure of the data is preserved. Conventional methods, such as NRP and PMRP, are recognised for their benefits; however, they also have limitations. NRP is generally accurate but can encounter difficulties with certain data types or noise. The PMRP is simple, but occasionally fails to capture complex relationships within the data. The performance of HRP is investigated through comprehensive evaluations, including simulations and real-world data analyses from key machine learning datasets, such as period change, toxicity, MNIST, and human activity recognition (HAR). We examine various factors such as sample size, dimensions of the original and reduced data, and sparsity. Distance distortion is the primary metric utilised to measure performance, and indicates how well the dataset structure is maintained after dimension reduction. In every test, HRP consistently demonstrates the least distance distortion, performing superiorly to the NRP and PMRP. This is true for a range of scenarios and datasets. HRP represents a significant advancement in dimension reduction techniques. Its ability to minimise information loss during the reduction process demonstrates its potential as a powerful tool for managing complex high-dimensional data in practical applications. This renders HRP an important development in the fields of machine learning, intelligent systems, and artificial intelligence, offering improved efficiency and precision in data analyses.

Optimization of Machining Parameters for Nimonic PE16 Using Machine Learning Models

Machining high-temperature alloys such as Nimonic PE16 demands precise control of machining parameters to achieve desired outcomes while minimizing tool wear and optimizing surface finish. In this study, we propose using machine learning regression models combined with synthetic data and response surface methodology strategies to optimize machining parameters for PE16. We aim to develop a predictive model that accurately estimates optimal cutting speeds and feed rates based on key output parameters, including cutting forces and surface roughness. Our methodology involves collecting experimental data from controlled machining tests conducted on PE16 samples under varying conditions. We used the datasets to train and validate regression models to establish correlations between input parameters and machining outcomes. The performance of each model is evaluated based on metrics such as mean absolute error and coefficient of determination. These metrics show relationships within the data and can determine a model’s success. The proposed machine learning framework offers a data-driven approach to optimize machining processes for PE16, facilitating enhanced efficiency, productivity, and quality in nuclear and other high-performance applications. Our findings contribute to understanding machining dynamics in challenging materials and provide valuable insights for intelligent machining systems.

NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites.

Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essential solution towards spike-based machine intelligence and neural learning systems. However, on-line learning capability for neuromorphic models is still an open challenge. In this study a novel neuromorphic architecture with dendritic on-line learning (NADOL) is presented, which is a novel efficient methodology for brain-inspired intelligence on embedded hardware. With the feature of distributed processing using spiking neural network, NADOL can cut down the power consumption and enhance the learning efficiency and convergence speed. A detailed analysis for NADOL is presented, which demonstrates the effects of different conditions on learning capabilities, including neuron number in hidden layer, dendritic segregation parameters, feedback connection, and connection sparseness with various levels of amplification. Piecewise linear approximation approach is used to cut down the computational resource cost. The experimental results demonstrate a remarkable learning capability that surpasses other solutions, with NADOL exhibiting superior performance over the GPU platform in dendritic learning. This study's applicability extends across diverse domains, including the Internet of Things, robotic control, and brain-machine interfaces. Moreover, it signifies a pivotal step in bridging the gap between artificial intelligence and neuroscience through the introduction of an innovative neuromorphic paradigm.

Analysis of using Internet of Things devices to manage the collection and transportation of municipal solid waste

The article analyzes the influence of M2M objects, the Internet of Things, robotics, and machine intelligence and vision systems on the municipal solid waste management system. It is demonstrated that goal-oriented, sustainable activity and development imply ensuring harmony in the homeostatic phase space of possible events. However, the life of society in the digital space is based on an increasingly expanding and often unexplored range of non-target manifestations of the development and application of information and telecommunication systems and technologies for working with digital data, carried out, among other things, with the support of government agencies.

Control System Design for Accurate Operation of Auxiliary Excavator Clusters

Abstract In recent years, with advancements in machine control automation and intelligent systems, both domestic and international research has increasingly focused on the automation and semi-automation of excavator control. This study introduces an auxiliary control system for excavator fleets, which utilizes sensor and wireless communication technologies. The research investigates the excavation errors that arise when operators rely on personal vision and experience in complex working environments, as well as the challenges of managing large fleets of excavators. By assisting operators in controlling the excavators, the system significantly reduces the operator’s experience requirements and work intensity and provides higher precision, consistency, and efficiency for excavation equipment. This method not only further improves the operating efficiency and excavation accuracy, but also saves the overall construction cost and improves the sustainability of the project.

Machine intelligence security : A methodological blend of fuzzy logic in industry 4.0 algorithms

The way things are made has changed a lot because of Industry 4.0. It has also led to a time with great technology and relationships. The paper discusses way to improve security in Machine Intelligence in the setting of Industry 4.0. The study uses a mix of methods to combine Fuzzy Logic with cutting-edge Industry 4.0 algorithms in order to deal with new hacking problems. Because fuzzy logic can deal with doubt and imprecision, it can be used to make current methods more reliable. This creates a complex and flexible security structure. The merger was carefully planned to make the methods for finding anomalies, reducing threats, and responding to incidents work better. The suggested method aims to make machine intelligence systems more resistant to complex cyber dangers by combining the best parts of Fuzzy Logic with Industry 4.0 algorithms. This study adds to the growing conversation about how to keep smart factory settings safe by focusing on a proactive and dynamic security model. The effects of this mix of methods could be felt in many different industries, making it possible to use advanced technologies in a safer and more reliable way in the age of Industry 4.0.

Embracing Robotics and Intelligent Machine Systems for Smart Agricultural Applications [From the Guest Editors

Embracing Robotics and Intelligent Machine Systems for Smart Agricultural Applications [From the Guest Editors

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.

Artificial intelligence and rehabilitation: what’s new and promising

The development of artificially intelligent technological machine systems that can integrate large volumes of data, and also ‘learn’ to recognize notable patterns, are currently being widely discussed and employed in various health and other realms. In this regard, what promise do these systems hold for ameliorating the late life chronic disease burden of increasing numbers of adults globally that may stem from one or multiple chronic longstanding health conditions. To explore this issue, a broad exploration of rehabilitation associated artificial intelligence implications was conducted using leading data bases. Results show that there are some active advances in both artificial intelligence and machine learning realms, but not in the context of desirable robust observations in all cases. Much future work is indicated though and is strongly recommended.

Differentially private knowledge transfer for federated learning

Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. However, model parameters may encode not only non-private knowledge but also private information of local data, thereby transferring knowledge via model parameters is not privacy-secure. Here, we present a knowledge transfer method named PrivateKT, which uses actively selected small public data to transfer high-quality knowledge in federated learning with privacy guarantees. We verify PrivateKT on three different datasets, and results show that PrivateKT can maximally reduce 84% of the performance gap between centralized learning and existing federated learning methods under strict differential privacy restrictions. PrivateKT provides a potential direction to effective and privacy-preserving knowledge transfer in machine intelligent systems.

Intelligent RGV Dynamic Scheduling Virtual Simulation Technology Based on Machine Learning

With the development of workshop automation, the complexity of RGV (Rail Guided Vehicle) dynamic scheduling schemes using virtual simulation technology is increasing. For the widely valued intelligent machining systems, machine learning based optimization algorithms can effectively respond to the increasingly complex RGV dynamic intelligent scheduling. In the whole model construction, how to complete the modular design of the intelligent processing system and optimize the solution is the key problem that needs to be solved urgently at present. This paper studied the use of particle swarm optimization to design the RGV dynamic scheduling model, aiming to improve the material processing production efficiency of RGV dynamic scheduling and reduce the system failure rate. Through problem modeling, solution and simulation experiment analysis, this paper applied particle swarm optimization based on machine learning, combined with RGV structure modular design and task parameter test set samples. According to the data results, the following conclusions can be drawn from the discussion. Under the background of intelligent logistics system, the RGV dynamic scheduling model using particle swarm optimization had higher material processing production efficiency than the traditional scheduling method in all job test samples, and the average increase was 13.25%. Meanwhile, in terms of system failures, optimization algorithms were better than traditional scheduling methods, with an average reduction of 4.6%. This shows that the RGV dynamic scheduling model based on particle swarm optimization has a better practical application effect.

A contemporary advancement of intelligent machining and sustainability aspects in hard machining area: A Critical Review

The intelligent manufacturing devotes considerable effort to towards machining process. This phenomenon is engendered by the growing demand for advanced machining process for manufacturing of precise parts by adopting optimization techniques. This article illustrates the most significant developments in the sustainability aspects as well as optimization and modelling techniques adopted to solve the problems and complexity in hard machining process. Machining realisation necessitates recent and future breakthroughs in technological innovations for Industry 4.0. A significant amount of focus is also paid to the different sustainability aspects, modelling strategies and performance analysis during hard machining process. Many avenues for future study on the needs of intelligent manufacturing are discussed in this article. The future directions for intelligent machine systems and sustainability factors are also discussed for the green, sustainable, and high dimensional accuracy manufacturing in hard machining area.

Applications of Artificial Intelligence Algorithms in the Energy Sector

The digital transformation of the energy sector toward the Smart Grid paradigm, intelligent energy management, and distributed energy integration poses new requirements for computer science. Issues related to the automation of power grid management, multidimensional analysis of data generated in Smart Grids, and optimization of decision-making processes require urgent solutions. The article aims to analyze the use of selected artificial intelligence (AI) algorithms to support the abovementioned issues. In particular, machine learning methods, metaheuristic algorithms, and intelligent fuzzy inference systems were analyzed. Examples of the analyzed algorithms were tested in crucial domains of the energy sector. The study analyzed cybersecurity, Smart Grid management, energy saving, power loss minimization, fault diagnosis, and renewable energy sources. For each domain of the energy sector, specific engineering problems were defined, for which the use of artificial intelligence algorithms was analyzed. Research results indicate that AI algorithms can improve the processes of energy generation, distribution, storage, consumption, and trading. Based on conducted analyses, we defined open research challenges for the practical application of AI algorithms in critical domains of the energy sector.

Data fusion of multiple machine intelligent systems for the condition assessment of subway structures

Water intrusion through soil is considered the most significant structural issue and the major cause of concrete degradation in subway networks. An enormous amount of water infiltration may expedite the deterioration mechanisms, such as moisture marks, spalling, scaling, and cracks. These mechanisms can compromise the structural durability and jeopardize people’s safety. The condition assessment of concrete infrastructure is predominantly conducted based of visual inspection techniques, which are costly, time-consuming, and error prone. In this research, two main models for the condition assessment of subway networks are proposed. First, image processing techniques and machine intelligent systems are streamlined through successive operations to detect and quantify multiple surface defects automatically. Spatial and frequency domain filters are used to enhance the image clues, in tandem with artificial neural networks (ANNs) and regression analysis (RA) for defect recognition. The Monte Carlo simulation (MCS) is then leveraged to deliver advanced optimization and accurate estimation for each defect’s condition index in the subway element. The developed method was implemented on four stations in Montréal subway systems, whereby the performance of ANNs and RA was validated through R2 as 0.928 and 0.957, respectively. Moreover, the MCS forecast precision was recorded as 95% percentile, which proves the efficacy of the developed models. This research provides insights for infrastructure managers about maintenance and intervention plans in order to prioritize their spending policies.

Dew point pressure of gas condensates, modeling and a comprehensive review on literature data

The accurate and in-time prediction of gas condensates dew point pressure (PDew) is of great importance regarding the technical and economic points of view for fluid characterization, reservoir performance calculations, planning the development of gas condensate reservoirs, and design and optimization of production systems. Although the laboratory tests provide the most accurate and reliable results, it is an expensive and time-consuming process sometimes associated with some errors. Artificial intelligence-based methods have emerged as promising tools in different aspects of engineering. In this study, after a thorough analysis of the gas condensate data samples, the application of different intelligent modeling is investigated. A databank of 721 data samples is gathered, and different intelligent methods approaches are used for modeling. The results of three different data mining methods are combined using Committee Machine Intelligent Systems (CMIS) in an attempt to receive more accurate results. Three different methods of arithmetic, geometric, and harmonic approaches are utilized to develop the CMIS model. It was concluded that the harmonic CMIS yields the best predictions by average absolute relative deviation (AARD) and R2 values of 3.456% and 0.9702, respectively. This novel CMIS model could successfully outperform all the developed initial models. Additionally, a literature review showed that the proposed model could outperform the previously published models including artificial intelligence, equation of state, and correlation-based method considering both prediction accuracy and data coverage.

Convolutional Neural Network-BO Based Feature Extraction and Multi-Layer Neural Network-SR Based Classification for Facial Expression Recognition

Facial expression recognition has been more essential in artificial machine intelligence systems in recent years. Recognizing facial expressions automatically has constantly been considered as a challenging task since people significantly vary the way of exhibiting their facial expressions. Numerous researchers established diverse approaches to analyze the facial expressions automatically but there arise few imprecision issues during facial recognition. To address such shortcomings, our proposed approach recognizes the facial expressions of humans in an effective manner. The suggested method is divided into three stages: pre-processing, feature extraction, and classification. The inputs are pre-processed at the initial stage and CNN-BO algorithm is used to extract the best feature in the feature extraction step. Then the extracted feature is provided to the classification stage where MNN-SR algorithm is employed in classifying the face expression as joyful, miserable, normal, annoyance, astonished and frightened. Also, the parameters are tuned effectively to obtain high recognition accuracy. In addition to this, the performances of the proposed approach are computed by employing three various datasets namely; CMU/VASC, Caltech faces 1999, JAFFE and XM2VTS. The performance of the proposed system is calculated and comparative analysis is made with few other existing approaches and its concluded that the proposed method provides superior performance with optimal recognition rate.

An Intelligent Detoxification Function of Liver Algorithm-Partial Transmit Sequence (IDFLA-PTS) for the Reduction of Peak to Average Power Ratio in Underwater Acoustic OFDM Communication

Intelligent algorithms in artificial intelligence have brought several benefits to digital signal processing. The boom in machine learning and intelligent systems provides new perspectives and methods to solve many research problems in Underwater Acoustic (UWA) Orthogonal Frequency Divisional Multiplexing (OFDM) communication. Partial transmit sequence is a tremendous technique for the mitigation of high Peak-to-Average Power Ratio (PAPR) in OFDM communication systems, but finding the optimum phase factors has still a few problems. In this paper, a Partial Transmit Sequence (PTS) based on an Intelligent Detoxification Function of Liver Algorithm-Partial Transmit Sequence (IDFLA-PTS) is proposed for the mitigation of PAPR in the UWA OFDM communication systems. This algorithm reduces the PAPR and the complexity of the proposed UWA OFDM model. The IDFLA-PTS is also compared with the Genetic Algorithm-Partial Transmit Sequence (GA-PTS). Besides this, the Bit Error Rate (BER) performance of the IDFLA-PTS is shown when a High Power Amplifier (HPA) is used for the BELLHOP channel model. The experimental results of the proposed IDFLA-PTS method achieved nearly optimum results with fair complexity as compared to GA-PTS and boosted the BER performance.

Machine Learning Application for Predicting Heart Attacks in Patients from Europe

Even today, there are still a large number of people suffering from heart attacks, which have already claimed numerous lives worldwide. To examine the main components of this problem in an objective and timely manner, we chose to work with a methodology that relies on taking and learning from real and existing data for use in training and testing predictive models. This was carried out to obtain useful data for the present research work. There are in parallel different methodologies that do not quite fit the model of this work. Data was collected from the "Center for Machine Learning and Intelligent Systems" which in turn contains data from patients who have ever suffered a cardiovascular attack and from patients who never suffered the disease, all of them being patients selected from different medical institutions. With the corresponding information, it was subjected to different processes such as cleaning, preparation, and training with the data, to obtain a logistic regression type automatic learning model ready to predict whether or not a person may suffer a cardiovascular attack. Finally, a result of 87% accuracy was obtained for people who suffered a heart attack and an accuracy of 81% for people who would not suffer from this disease. This can greatly reduce the mortality rate due to infarction, by knowing the condition of a person who is unaware of his or her health situation and thus being able to take appropriate measures.

Intelligent systems in obstetrics and midwifery: Applications of machine learning.

INTRODUCTIONMachine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics.METHODSA thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted.RESULTSThirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc.CONCLUSIONSThis systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

On the evaluation of solubility of hydrogen sulfide in ionic liquids using advanced committee machine intelligent systems

Ionic Liquids (ILs) are increasingly emerging as new innovating green solvents with great importance from academic, industrial, and environmental perspectives. This surge of interest in considering ILs in various applications is owed to their attractive properties. Involvements in the gas sweetening and the reduction of the amounts of sour and acid gasses are among the most promising applications of ILs. In this study, new advanced committee machine intelligent systems (CMIS) were introduced for predicting the solubility of hydrogen sulfide (H2S) in various ILs. The implemented CMIS models were gained by linking robust data-driven techniques, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath rigorous schemes using group method of data handling (GMDH) and genetic programming (GP). The proposed paradigms were developed using an extensive database encompassing 1243 measurements of H2S solubility in 33 ILs. The performed comprehensive error investigation revealed that the newly implemented paradigms yielded very satisfactory prediction performance. Besides, it was found that CMIS-GP provided more accurate estimations of H2S solubility in ILs compared with both the other intelligent models and the best-prior paradigms. In this regard, the developed CMIS-GP exhibited overall average absolute relative deviation (AARD) and coefficient of determination (R2) values of 2.3767% and 0.9990, respectively. Lastly, the trend analyses demonstrated that the tendencies of CMIS-GP predictions were in excellent accordance with the real variations of H2S solubility in ILs with respect to pressure and temperature.