Enable Machine Learning Research Articles

Intelligent super-fast Vehicle-to-Everything 5G communications with predictive switching between mmWave and THz links

With the incoming of 5G communications, Vehicular Networks have the hope to achieve ultra-high data transmission rate with extremely low end-to-end delay. However, the dynamic nature of transportation traffic and increased data bandwidth demands are the major obstacles to achieve high transmission rate in Vehicular-to-Anything (V2X) Networks. To overcome these obstacles, this paper presents a novel Software Defined Networking (SDN)-controlled and Cognitive Radio (CR)-enabled V2X routing approach to achieve ultra-high data rate, by using predictive V2X routing that supports the intelligent switching between two 5G technologies: millimeter-wave (mmWave) and terahertz (THz). To improve the network management, Road Side units (RSUs) are used to segregate the V2X network into different clusters. Stability-aware clustering (SAC) scheme is also used for cluster formations. Our intelligent V2X is based on three features enabled machine learning approach: (1) To predict future 3D positions of the vehicles in the Cluster Heads (CHs) using Deep Neural Network with Extended Kalman Filter (DNN-EKF) algorithm for real-time, high-resolution prediction. (2) For THz communications, 0.3 THz to 3 THz band is selected for short-distance super-fast data transmissions. The THz band detection is performed by the CR-enabled Road Side Units (cRSUs). A Genetic Algorithm (GA)-based Improved Fruit Fly (GA-IFF) scheme is proposed to achieve an optimal route selection in THz communications. (3) In mmWave-based V2X communications, optimal beam selection is performed by the multi-type2 fuzzy inference system (M-T2FIS). By using these three intelligent designs approaches, we are able to achieve ultrahigh- rate and minimized transmission delay for short-range (in THz bands) and middle-range (in mmWave) communications. Finally, the proposed SDN-controlled, CR-enabled V2X Network is modeled and tested for performance evaluations with the metrics of delivery ratio, routing delay, protocol overhead, and data rate.

Machine Learning Technique Using the Signature Method for Automated Quality Control of Argo Profiles

AbstractA profile from the Argo ocean observation array is a sequence of three‐dimensional vectors composed of pressure, salinity, and temperature, appearing as a continuous curve in three‐dimensional space. The shape of this curve is faithfully represented by a path signature, which is a collection of all the iterated integrals. Moreover, the product of two terms of the signature of a path can be expressed as the sum of higher‐order terms. As a result of this algebraic property, a nonlinear function of the profile shape can always be represented by a weighted linear combination of the iterated integrals, which enables machine learning of a complicated function of the profile shape. In this study, we performed supervised learning for existing Argo data with quality control flags by using the signature method and demonstrated the estimation performance by cross validation. Unlike rule‐based approaches, which require several complicated and possibly subjective rules, this method is simple and objective in nature because it relies only on past knowledge regarding the shape of profiles. This technique is critical for realizing automatic quality control for Argo profile data.

Multilanguage-handwriting self-powered recognition based on triboelectric nanogenerator enabled machine learning

Handwriting signature is widely used and the main challenge for handwriting recognition is how to obtain comprehensive handwriting information. Triboelectric nanogenerator is sensitive to external triggering force and can be used to record personal handwriting signals and associated characteristics. In this work, micro/nano structure textured TENG acting as a smart self-powered handwriting pad is developed and its effectiveness for handwriting recognition is demonstrated. Three individuals' handwriting signals of English words, Chinese characters and Arabic numerals are acquired by leaf-inspired TENG, and the other three people's handwriting signals of English sentences and the corresponding Chinese sentences are obtained by cylindrical microstructured PDMS based TENG, and these signals exhibit unique features. Combined with the machine learning method, the people's handwriting was successfully identified. The classification accuracies of 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% were reached for English words, Arabic numerals, Chinese characters, English sentences, and the corresponding Chinese sentences, respectively. The results strongly suggested that the textured TENG exhibited great potential in personal handwriting signature identification, security defense, and private information protection applications. This work presented a killer approach for multilanguage handwriting signal sensing and recording. We reported a smart self-powered handwriting pad based on micro/nano-structures textured TENG for sensing different individuals’ handwriting. The obtained signals exhibited unique features both in time domain and frequency domain which could be used in personal handwriting signature identification. For the first time, a novel solution for recognizing personal handwriting combing TENG based smart self-powered device, advanced signal processing and machine learning method was reported. We successfully demonstrated that extremely high classification accuracies achieved by using the proposed advanced technology. • TENG based self-powered handwriting pad was designed and fabricated. • The handwriting pad was sensitive and rapidly-responding to handwriting behavior. • Handwriting was recognized by combing the handwriting pad and machine learning method. • Extremely high classification accuracies were obtained for three kinds of characters.

Precision Health-Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation.

BackgroundEmerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited.ObjectiveThe objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services.MethodsWe used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models.ResultsDecision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P<.001, AUROC P=.01; social work: F1 score P<.001, AUROC P=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider.ConclusionsPrecision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

Privacy Risk Awareness in Wearables and the Internet of Things

Day to day interactions with wearable and pervasive systems lead to collected data that capture various aspects of human behavior and enable machine learning algorithms to extract extensive information about users. We discuss privacy risk awareness, and ways to preserve privacy and integrate it in current frameworks.

A Quantum Mechanics-Based Framework for EEG Signal Feature Extraction and Classification

Quantum machine learning (QML) is an emerging research field, which is devoted to devising and implementing quantum algorithms that could enable machine learning faster than that of classical computers. In this article, a hierarchic quantum mechanics-based framework is investigated to implement both the feature extraction and classification in the electroencephalogram (EEG) signal. First, the classical EEG signal dataset is prepared as a quantum state while the sign of the data point is preserved. The prepared quantum state is then evolved with the quantum wavelet packet transformation (QWPT) and the wavelet packet energy entropy (WPEE) feature is extracted as the input of the subsequent quantum classifier. We finally propose the improved quantum support vector machine with the arbitrary nonlinear kernel, which is employed to predict the label of the EEG signal. The complexity analysis indicates that the proposed framework provides exponential speedup over the same structured classical counterpart. Besides, the quantitative experimental results verify the feasibility and validity.

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic and firmware implementations. This paper presents FANN-on-MCU, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-V-based Parallel Ultra-Low-Power (PULP) platform. The toolkit takes multi-layer perceptrons trained with FANN and generates code targeted at execution on low-power microcontrollers either with a floating-point unit (i.e., ARM Cortex-M4F and M7F) or without (i.e., ARM Cortex M0-M3 or PULP-based processors). This paper also provides an architectural performance evaluation of neural networks on the most popular ARM Cortex-M family and the parallel RISC-V processor called Mr. Wolf. The evaluation includes experimental results for three different applications using a self-sustainable wearable multi-sensor bracelet. Experimental results show a measured latency in the order of only a few microseconds and a power consumption of few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22x and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.

Deep learned tissue \u201cfingerprints\u201d classify breast cancers by ER/PR/Her2 status from H&E images

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of “tissue fingerprints,” which leverages large, unannotated datasets in a label-free manner to learn H&E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call “fingerprints,” to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.

Revealing cytotoxic substructures in molecules using deep learning

In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical advances and the ever growing amount of available toxicity data enabled machine learning, especially neural networks, to impact the field of predictive toxicology. In this study, cytotoxicity prediction, one of the earliest handles in drug discovery, is investigated using a deep learning approach trained on a highly consistent in-house data set of over 34,000 compounds with a share of less than 5% of cytotoxic molecules. The model reached a balanced accuracy of over 70%, similar to previously reported studies using Random Forest. Albeit yielding good results, neural networks are often described as a black box lacking deeper mechanistic understanding of the underlying model. To overcome this absence of interpretability, a Deep Taylor Decomposition method is investigated to identify substructures that may be responsible for the cytotoxic effects, the so-called toxicophores. Furthermore, this study introduces cytotoxicity maps which provide a visual structural interpretation of the relevance of these substructures. Using this approach could be helpful in drug development to predict the potential toxicity of a compound as well as to generate new insights into the toxic mechanism. Moreover, it could also help to de-risk and optimize compounds.

Brain-Mediated Transfer Learning of Convolutional Neural Networks

The human brain can effectively learn a new task from a small number of samples, which indicates that the brain can transfer its prior knowledge to solve tasks in different domains. This function is analogous to transfer learning (TL) in the field of machine learning. TL uses a well-trained feature space in a specific task domain to improve performance in new tasks with insufficient training data. TL with rich feature representations, such as features of convolutional neural networks (CNNs), shows high generalization ability across different task domains. However, such TL is still insufficient in making machine learning attain generalization ability comparable to that of the human brain. To examine if the internal representation of the brain could be used to achieve more efficient TL, we introduce a method for TL mediated by human brains. Our method transforms feature representations of audiovisual inputs in CNNs into those in activation patterns of individual brains via their association learned ahead using measured brain responses. Then, to estimate labels reflecting human cognition and behavior induced by the audiovisual inputs, the transformed representations are used for TL. We demonstrate that our brain-mediated TL (BTL) shows higher performance in the label estimation than the standard TL. In addition, we illustrate that the estimations mediated by different brains vary from brain to brain, and the variability reflects the individual variability in perception. Thus, our BTL provides a framework to improve the generalization ability of machine-learning feature representations and enable machine learning to estimate human-like cognition and behavior, including individual variability.

Malignancy prediction among tissues from Oral SCC patients including neck invasions: a 1H HRMAS NMR based metabolomic study.

Oral cancer is a sixth commonly occurring cancer globally. The use of tobacco and alcohol consumption are being considered as the major risk factors for oral cancer. The metabolic profiling of tissue specimens for developing carcinogenic perturbations will allow better prognosis. To profile and generate precise 1H HRMAS NMR spectral and quantitative statistical models of oral squamous cell carcinoma (OSCC) in tissue specimens including tumor, bed, margin and facial muscles. To apply the model in blinded prediction of malignancy among oral and neck tissues in an unknown set of patients suffering from OSCC along with neck invasion. Statistical models of 1H HRMAS NMR spectral data on 180 tissues comprising tumor, margin and bed from 43 OSCC patients were performed. The combined metabolites, lipids spectral intensity and concentration-based malignancy prediction models were proposed. Further, 64 tissue specimens from twelve patients, including neck invasions, were tested for malignancy in a blinded manner. Forty-eight metabolites including lipids have been quantified in tumor and adjacent tissues. All metabolites other than lipids were found to be upregulated in malignant tissues except for ambiguous glucose. All of three prediction models have successfully identified malignancy status among blinded set of 64 tissues from 12 OSCC patients with an accuracy of above 90%. The efficiency of the models in malignancy prediction based on tumor induced metabolic perturbations supported by histopathological validation may revolutionize the OSCC assessment. Further, the results may enable machine learning to trace tumor induced altered metabolic pathways for better pattern recognition. Thus, it complements the newly developed REIMS-MS iKnife real time precession during surgery.

Ethical dilemmas posed by mobile health and machine learning in psychiatry research.

The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models to process such information has raised novel ethical dilemmas regarding researchers’ duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. Ultimately, the hope is that this review will facilitate continued discussion on how to achieve best practice in mobile health research within psychiatry.

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy.

Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things.

The field of photovoltaics gives the opportunity to make our buildings ‘‘smart’’ and our portable devices “independent”, provided effective energy sources can be developed for use in ambient indoor conditions. To address this important issue, ambient light photovoltaic cells were developed to power autonomous Internet of Things (IoT) devices, capable of machine learning, allowing the on-device implementation of artificial intelligence. Through a novel co-sensitization strategy, we tailored dye-sensitized photovoltaic cells based on a copper(ii/i) electrolyte for the generation of power under ambient lighting with an unprecedented conversion efficiency (34%, 103 μW cm−2 at 1000 lux; 32.7%, 50 μW cm−2 at 500 lux and 31.4%, 19 μW cm−2 at 200 lux from a fluorescent lamp). A small array of DSCs with a joint active area of 16 cm2 was then used to power machine learning on wireless nodes. The collection of 0.947 mJ or 2.72 × 1015 photons is needed to compute one inference of a pre-trained artificial neural network for MNIST image classification in the employed set up. The inference accuracy of the network exceeded 90% for standard test images and 80% using camera-acquired printed MNIST-digits. Quantization of the neural network significantly reduced memory requirements with a less than 0.1% loss in accuracy compared to a full-precision network, making machine learning inferences on low-power microcontrollers possible. 152 J or 4.41 × 1020 photons required for training and verification of an artificial neural network were harvested with 64 cm2 photovoltaic area in less than 24 hours under 1000 lux illumination. Ambient light harvesters provide a new generation of self-powered and “smart” IoT devices powered through an energy source that is largely untapped.

Topographic brain tumor anatomy drives seizure risk and enables machine learning based prediction

ObjectiveThe aim of this study was to identify relevant risk factors for epileptic seizures upon initial diagnosis of a brain tumor and to develop and validate a machine learning based prediction to allow for a tailored risk-based antiepileptic therapy. MethodsClinical, electrophysiological and high-resolution imaging data was obtained from a consecutive cohort of 1051 patients with newly diagnosed brain tumors. Factor-associated seizure risk difference allowed to determine the relevance of specific topographic, demographic and histopathologic variables available at the time of diagnosis for seizure risk. The data was divided in a 70/30 ratio into a training and test set. Different machine learning based predictive models were evaluated before a generalized additive model (GAM) was selected considering its traceability while maintaining high performance. Based on a clinical stratification of the risk factors, three different GAM were trained and internally validated. ResultsA total of 923 patients had full data and were included. Specific topographic anatomical patterns that drive seizure risk could be identified. The involvement of allopallial, mesopallial or primary motor/somatosensory neopallial structures by brain tumors results in a significant and clinically relevant increase in seizure risk. While topographic input was most relevant for the GAM, the best prediction was achieved by a combination of topographic, demographic and histopathologic information (Validation: AUC: 0.79, Accuracy: 0.72, Sensitivity: 0.81, Specificity: 0.66). ConclusionsThis study identifies specific phylogenetic anatomical patterns as epileptic drivers. A GAM allowed the prediction of seizure risk using topographic, demographic and histopathologic data achieving fair performance while maintaining transparency.

Artificial Intelligence, Machine Learning, and Cardiovascular Disease.

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

Intelligent wireless communication establishment with fault free IoT enabled machine learning strategies

In the modernized world, each and every individual requires smart gadget connectivity and the tremendous increment of such devices leads a growth of the world further. The predictable thing in future is approximately trillion and more smart gadgets will be there to establish the connection to server via Internet medium. The above quoted statements clearly illustrate the need of internet and wireless communications as well as its importance in future. A trending way to establish the internet over smart gadgets is by means of Internet-of-Things (IoT), which establishes a bridge in between gadgets and server through internet medium. With the help of such IoT device we can emulate the connections between client and server easily without any delay. The client end needs support like robustness, security, easy to connect between entities and privacy preservation with fault-free nature. The technologies are really an important medium to people to accomplish their communication needs, but the security threats and fault identifications over communication is the major concern to deal with such type of communications around internet medium world-wide. So, that this proposed approach mainly concentrate on fault identification mechanism and trust enabled communications with the help of Internet-of-Things (IoT) with powerful machine learning technique called Deep-cNN (DcNN), and the integration of both these techniques assist the proposed communication strategy well and it is combinely named as “DcNNIoT”. The proposed system aims to provide robust communication facility with Quality of Service enabled features along with fault free and trust worthy mode. The proposed result shows that the implementation of DcNNIoT provides maximized network lifetime and reduced network delay around entities.

SNOMED CT-Based Standardized e-Clinical Pathways for Enabling Big Data Analytics in Healthcare

Automation of healthcare facilities represents a challenging task of streamlining a highly information-intensive sector. Modern healthcare processes produce large amounts of data that have great potential for health policymakers and data science researchers. However, a considerable portion of such data is not captured in electronic format and hidden inside the paperwork. A major source of missing data in healthcare is paper-based clinical pathways (CPs). CPs are healthcare plans that detail the interventions for the treatment of patients, and thus are the primary source for healthcare data. However, most CPs are used as paper-based documents and not fully automated. A key contribution towards the full automation of CPs is their proper computer modeling and encoding their data with international clinical terminologies. We present in this research an ontology-based CP automation model in which CP data are standardized with SNOMED CT, thus enabling machine learning algorithms to be applied to CP-based datasets. CPs automated under this model contribute significantly to reducing data missingness problems, enabling detailed statistical analyses on CP data, and improving the results of data analytics algorithms. Our experimental results on predicting the Length of Stay (LOS) of stroke patients using a dataset resulting from an e-clinical pathway demonstrate improved prediction results compared with LOS prediction using traditional EHR-based datasets. Fully automated CPs enrich medical datasets with more CP data and open new opportunities for machine learning algorithms to show their full potential in improving healthcare, reducing costs, and increasing patient satisfaction.

Role of Machine Learning in Manufacturing Sector

This paper discusses the basic concept Machine learning and its techniques, algorithms as well the impact of Machine Learning in Manufacturing processes and Industrial Production. There has been an unprecedented increase in the data available in the last couple of decades. This has enabled machine learning to be applied in various fields. Machine learning is field of study which enables the computer system to learn automatically as well as improve from experience and perform various tasks without explicit instructions. The primary aim of machine learning is to allow the computers learn automatically without human assistance or intervention and adjust actions accordingly It is being employed in many fields. Manufacturing one area where machine learning is very useful. This paper discusses the various areas where machine learning can improve the process of manufacturing like predictive maintenance, process optimisation, quality control, scheduling of resources among others. This can be done by employing various machine learning techniques and algorithms using concepts such as deep learning, neural networks, supervised, unsupervised and reinforcement learning. The relationship of how machines and humans can co-exist and work together to improve the efficiency of production is also discussed. Industry 4.0 or fourth revolution that has occurred in manufacturing which deals with advent of automation in manufacturing industry and its significance is discussed. We take a look at the various benefits and applications of machine learning in the field of manufacturing engineering. This paper also discusses the various challenges and future scope of employing machine learning in the manufacturing.

Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling

Abstract Accurate predictions of solar Photovoltaic (PV) power generation at different time scales are essential for reliable operations of energy management systems. Contemporary methods can accurately predict solar PV power a few steps ahead but fail to maintain high level of prediction accuracy as the number of steps increases. In this paper, we present a simple but effective univariate approach to predict solar PV power output multiple steps ahead. The objective is to maintain high level accuracy of the machine learning algorithms for multiple steps ahead prediction. The novelty of the proposed approach lies in the integration of a data re-sampling technique with machine learning algorithms. The data re-sampling technique enables machine learning algorithms to compute multiple steps ahead predictions via simple single-step ahead prediction on the re-sampled time-series. At each prediction step, the proposed approach first obtains a new representation of the original time series based on the re-sampling process. A separate prediction model is then implemented for single-step prediction on the re-sampled time series. The single-step ahead prediction of re-sampled time series corresponds to m-th step ahead prediction of original time series as determined by the re-sampling factor m. Since the proposed approach computes multiple steps ahead predictions by combining a set of models designed for single-step ahead prediction, it significantly reduces the error at higher prediction steps. We have evaluated the effectiveness of the proposed approach for 5-min to 3-h ahead predictions using 2 years of data from a 1.22 MW PV system in Australia.