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Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on $17$ benchmark DNA modifications prediction datasets of $12$ different species using $10$ different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing $32$ different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Skin cancer is a prevalent and perilous form of cancer and presents significant diagnostic challenges due to its high costs, dependence on medical experts, and time-consuming procedures. The existing diagnostic process is inefficient and expensive, requiring extensive medical expertise and time. To tackle these issues, researchers have explored the application of artificial intelligence (AI) tools, particularly machine learning techniques such as shallow and deep learning, to enhance the diagnostic process for skin cancer. These tools employ computer algorithms and deep neural networks to identify and categorize skin cancer. However, accurately distinguishing between skin cancer and benign tumors remains challenging, necessitating the extraction of pertinent features from image data for classification. This study addresses these challenges by employing Principal Component Analysis (PCA), a dimensionality-reduction approach, to extract relevant features from skin images. Additionally, accurately classifying skin images into malignant and benign categories presents another obstacle. To improve accuracy, the AdaBoost algorithm is utilized, which amalgamates weak classification models into a robust classifier with high accuracy. This research introduces a novel approach to skin cancer diagnosis by integrating Principal Component Analysis (PCA), AdaBoost, and EfficientNet B0, leveraging artificial intelligence (AI) tools. The novelty lies in the combination of these techniques to develop a robust and accurate system for skin cancer classification. The advantage of this approach is its ability to significantly reduce costs, minimize reliance on medical experts, and expedite the diagnostic process. The developed model achieved an accuracy of 93.00% using the DermIS dataset and demonstrated excellent precision, recall, and F1-score values, confirming its ability to correctly classify skin lesions as malignant or benign. Additionally, the model achieved an accuracy of 91.00% using the ISIC dataset, which is widely recognized for its comprehensive collection of annotated dermoscopic images, providing a robust foundation for training and validation. These advancements have the potential to significantly enhance the efficiency and accuracy of skin cancer diagnosis and classification. Ultimately, the integration of AI tools and techniques in skin cancer diagnosis can lead to cost reduction and improved patient outcomes, benefiting both patients and healthcare providers. [ABSTRACT FROM AUTHOR]

The issue of signal outages in sub-THz frequency communication for future 6G networks is addressed by this research. A machine learning method is proposed, employing Random Forest and K-Means algorithms to predict the optimal frequency band and outage probabilities for UAV relays. Both space and frequency diversity are explored to enhance signal strength, and metasurfacecarrying UAVs are introduced with a 16 × 16 mm² design. This design significantly reduces the predicted outage probability from 0.1% to 0.0178%. Finally, triangular and hexagonal UAV swarm formations with metasurfaces are investigated, demonstrating improved performance through heatmap results. [ABSTRACT FROM AUTHOR]

Heart disease (HD) is a major threat to human health, and the medical field generates vast amounts of data that doctors struggle to effectively interpret and use. Early prediction and classification of HD types are crucial for effective medical treatment. Researchers have found it important to use learning-based techniques from machine and deep learning, such as supervised and deep neural networks, to develop automatic models for HD. These techniques have been used to simulate HD management and extract important features from complex data sets. This survey examines various HD prediction models, classifying the learning-based techniques, datasets, and contexts used, and analyzing the performance metrics of each contribution. It also clarifies which method suits a type of HD. With the growth of data sets, researchers are increasingly utilizing these techniques to create more precise models. However, there is still much work to be done to improve the accuracy of HD predictions. [ABSTRACT FROM AUTHOR]

Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, autoimmune diseases and carcinogenesis. Identification of NT modification supports development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. This paper presents the NTpred framework that is competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, which is used to train a Logistic Regression classifier. On the BD1 benchmark dataset, the proposed framework outperforms the existing best-performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on the BD2 benchmark dataset, the proposed framework outperforms the existing best-performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC. NTpred is publicly available for further experimentation and predictive use at: https://sds_genetic_analysis.opendfki.de/PredNTS/. [ABSTRACT FROM AUTHOR]

The approval of retrieving information on the Internet originates several network securities matters. Intrusion recognition is a critical study in network security to spot unauthorized admission or occurrences on protected networks. Intrusion detection has a fully-fledged reputation in the current era. Research emphasizes several datasets to upsurge system precision and lessen the false-positive proportion. This article proposes a new intrusion detection system using big data analytics and deep learning to address some of the misuse and irregularity detection limitations. The proposed method could identify any odd activities in a network to recognize malicious or unauthorized action and permit a response during a confidentiality break. The proposed system utilizes the big data analytics platform based on parallel and distributed mechanisms. The parallel and distributed platforms improve the training time along with the accuracy. The experimentation appropriately classifies the information as either normal or abnormal. The proposed system has a recognition proportion of 96.11% that pointedly expands overall recognition accuracy related to existing strategies. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) is a popular approach to reducing distributional discrepancies between labeled source and the unlabeled target domain (TD) in machine learning. However, current UDA approaches often align feature distributions between two domains explicitly without considering the target distribution and intra-domain category information, potentially leading to reduced classifier efficiency when the distribution between training and test sets differs. To address this limitation, we propose a novel approach called Cluster Matching-based Improved Kernel Fisher criterion (CM-IKFC) for object classification in image analysis using machine learning techniques. CM-IKFC generates accurate pseudo-labels for each target sample by considering both domain distributions. Our approach employs K-means clustering to cluster samples in the latent subspace in both domains and then conducts cluster matching in the TD. During the model component training stage, the Improved Kernel Fisher Criterion (IKFC) is presented to extend cluster matching and preserve the semantic structure and class transitions. To further enhance the performance of the Kernel Fisher criterion, we use a normalized parameter, due to the difficulty in solving the characteristic equation that draws inspiration from symmetry theory. The proposed CM-IKFC method minimizes intra-class variability while boosting inter-class variants in all domains. We evaluated our approach on benchmark datasets for UDA tasks and our experimental findings show that CM-IKFC is superior to current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Heart disease (HD) has become a dangerous problem and one of the most significant mortality factors worldwide, which requires an expensive and sophisticated detection process. Most people are affected due to the failure of the heart which seriously threatens their lives due to high morbidity and mortality. Therefore, accurate prediction and diagnosis are needed for early prevention, detection, and treatment to reduce the death threats to human life. However, an early and accurate prediction of HD is still a challenging task to be addressed. In this work, we propose a machine learning-based prediction model (MLbPM) that exploits a combination of the data scaling methods, the split ratios, the best parameters, and the machine learning algorithms for predicting HD. The performance of the proposed model is tested by performing experiments on a University of California Irvine HD dataset to indicate the presence or absence of HD. The results show that the proposed MLbPM provides an accuracy of 96.7% when logistic regression, robust scaler, best parameter, and 70 : 30 as a split ratio of the dataset are considered. In addition, MLbPM outperforms other compared works in terms of accuracy. [ABSTRACT FROM AUTHOR]

This study aims to investigate the applications of Artificial Intelligence (AI) in the university libraries of Pakistan. Following the explanatory sequential mixed-methods approach, this study was completed in two phases. In the first phase, quantitative data was collected from 237 university librarians from across Pakistan. In the second phase, 10 purposefully selected university librarians were interviewed. The results reveal that Pakistani university libraries are using limited AI-based library services including text-to-speech and speech-to-text technologies, Google Assistant to search by voice command, Radio Frequency Identification (RFID) system for self-checkout, check-in, and security purposes, and intelligent data analysis for collection management. This study explored key factors that influence implementation of AI applications including highly integrated technological infrastructure, funding/cost associated with AI, collaboration between AI experts and professionals, and library users' feedback. The major identified challenges were the requirement of a highly networked and integrated environment, lack of budget, high cost of AI technologies, and lack of staff expertise. This study claims to implement the first comprehensive survey of AI applications in Pakistani university libraries at the national level. The research findings would support library and university administrators to integrate AI applications in libraries not only in Pakistan but also in other developing countries. [ABSTRACT FROM AUTHOR]

Accurate prediction of deoxyribonucleic acid (DNA) modifications is essential to explore and discern the process of cell differentiation, gene expression and epigenetic regulation. Several computational approaches have been proposed for particular type-specific DNA modification prediction. Two recent generalized computational predictors are capable of detecting three different types of DNA modifications; however, type-specific and generalized modifications predictors produce limited performance across multiple species mainly due to the use of ineffective sequence encoding methods. The paper in hand presents a generalized computational approach "DNA-MP" that is competent to more precisely predict three different DNA modifications across multiple species. Proposed DNA-MP approach makes use of a powerful encoding method "position specific nucleotides occurrence based 117 on modification and non-modification class densities normalized difference" (POCD-ND) to generate the statistical representations of DNA sequences and a deep forest classifier for modifications prediction. POCD-ND encoder generates statistical representations by extracting position specific distributional information of nucleotides in the DNA sequences. We perform a comprehensive intrinsic and extrinsic evaluation of the proposed encoder and compare its performance with 32 most widely used encoding methods on |$17$| benchmark DNA modifications prediction datasets of |$12$| different species using |$10$| different machine learning classifiers. Overall, with all classifiers, the proposed POCD-ND encoder outperforms existing |$32$| different encoders. Furthermore, combinedly over 5-fold cross validation benchmark datasets and independent test sets, proposed DNA-MP predictor outperforms state-of-the-art type-specific and generalized modifications predictors by an average accuracy of 7% across 4mc datasets, 1.35% across 5hmc datasets and 10% for 6ma datasets. To facilitate the scientific community, the DNA-MP web application is available at https://sds_genetic_analysis.opendfki.de/DNA_Modifications/. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the potential of Machine Vision (MV) approaches for the classification and identification of 12 varieties of guava. There are leaf images of the 12 local varieties of guava (Psidium guajava) that include Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. A digital camera captured these images of guava varieties in a natural environment. Multi-features were extracted from these images. It was a composite of histograms, binary features, textures, rotational, spectral, and translational features (RST). Total 47 multi-features were collected for each non-overlapping guava leaf image, i.e., 256 × 256 and 512 × 512 more, the supervised correlation-based feature selection (CFS) method with the best search algorithm was used to optimize 18 features instead of 47 multi-features. Several ML classifiers, including Instant base Identifier (IBI), Random Forest (RF), and Meta Bagging (MB), using 10-fold cross-validation, were applied to the optimized multi-features. IBI results performed better than other classifiers with an average overall accuracy of 93.01% on AOIs, 512 × 512 . In addition, IBI detected 90.5%, 89.5%, 94%, 97%, 95.5%, 97%, 99%, 96.5%, 99%, 80.5%, 88%, and 81.5% accuracy values for the 12 varieties of guava leaves, namely Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. The proposed study could play a significant role for the early and accurate identification of Guava varieties, and it would also be helpful for export quality measures for the national economy of the country. [ABSTRACT FROM AUTHOR]

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs. [ABSTRACT FROM AUTHOR]

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework "LGCA-VHPPI" is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets. [ABSTRACT FROM AUTHOR]

• AI based modeling of relative vibration of a steam turbine shaft bearing is presented. • The effect of operating parameters on the bearing vibration is studied. • AI model based operational strategy for vibration reduction of the steam turbine shaft bearing is developed. • The vibration reduction strategy is implemented at the power plant. • Vibration values lesser than the alarm limit are confirmed. The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Does feature selection methods enhance predictive performance of ML classifiers? • Which is the optimal combination of feature selection method and ML classifier? • Dose deep learning classifiers require external feature engineering? • Can feature selection along with attention makes better predictive pipeline? Requirements classification is a key component of software development life cycle. It enhances our understanding about project requirements, which in turn enables us to effectively identify and mitigate risks that could lead to project failure. Existing requirements classification predictors do not utilize feature selection methods competence in their predictive pipelines and lack in performance. To empower the process of automatic requirements classification, contributions of this paper are manifold. Firstly, it explores the potential of 7 filter-based feature selection techniques and 11 traditional machine learning classifiers. Secondly, for the first time it investigates combined potential of traditional feature selection and 9 diverse types of deep learning predictors. Thirdly, it presents a hybrid computational predictor namely FNReq-Net that reaps combine benefits of traditional feature selection and a novel deep learning predictor based on attention mechanism. Over two public benchmark datasets, large-scale experimental results reveal feature selection not only improves predictive performance of traditional machine learning predictors, but it also improves performance of deep learning predictors. The proposed FNReq-Net predictor outperforms state-of-the-art functional and non-functional requirements classification predictors by 4% and 1% in terms of F1-score over Promise and Promise-exp datasets, respectively. [ABSTRACT FROM AUTHOR]

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation. [ABSTRACT FROM AUTHOR]

Discrimination of circular RNA from long non-coding RNA is important to understand its role in different biological processes, disease prediction and cure. Identifying circular RNA through manual laboratories work is expensive, time-consuming and prone to errors. Development of computational methodologies for identification of circular RNA is an active area of research. State-of-the-art circular RNA identification methodologies make use of handcrafted features, which not only increase the feature space, but also extract irrelevant and redundant features. The paper in hand proposes an end-to-end deep learning-based framework named as CircNet, which does not require any handcrafted features. It takes raw RNA sequence as an input and utilises encoder–decoder based convolutional operations to learn lower-dimensional latent representation. This latent representation is further passed to another convolutional architecture to extract discriminative features followed by a classification layer. We performed extensive experimentation to highlight different regions of genome sequence that preserve the most important information for identifying circular RNAs. CircNet significantly outperforms state-of-the-art approaches with a considerable margin 10.29% in terms F1 measure. [ABSTRACT FROM AUTHOR]

• Emphasizing security challenges and requirements of IoT-based systems. • Examining ML as a security measure for the IoT domain and the challenges it poses on IoT. • Analyzing constraints of the prevailing ML security techniques proposed for IoT domain security. Internet of Things (IoT) is a pervasively-used technology for the last few years. IoT technologies are also responsible for intensifying various everyday smart applications improving the standard of living. However, the inter-crossing of IoT systems and the multi-directional elements responsible for these systems' placement have raised new safety concerns. They generate and share a massive amount of sensitive data. Unfortunately, both the data and the devices are susceptible to many privacy and security challenges. Much research has been done to secure these infrastructures; however, Machine Learning (ML), among others, provides higher accuracy. This survey covers the major security issues and open challenges encountered by IoT infrastructures. It also encompasses an in-depth study and analysis of ML-based state-of-the-art solutions used in securing such domains. The security challenges and requirements in IoT-based systems have been highlighted, along with a discussion on how ML supports security measures in the said domain. Furthermore, the challenges associated with ML-based security solutions have been identified concerning IoT. An analysis of prevailing ML security techniques' constraints is also contemplated. [ABSTRACT FROM AUTHOR]

Short-term solar irradiance forecasting plays a pivotal role in the effective integration of significantly fluctuating solar power into power grids. Existing computational approaches lack to investigate which climate parameter/s influence the most in attaining the optimal forecasting performance. The paper in hand utilizes diverse feature selection approaches to find the optimal subset of features. Using selected subset of features, a rigorous experimentation is performed with 12 adopted machine learning and 10 newly developed deep learning based regressors for most reliable global horizontal irradiance measurements of 9 different regions of Pakistan using 4 evaluation measures. Further, to attain better predictive performance of solar irradiance, we reap the benefits of different individual regressors and present a robust multi regional meta-regressor. Among machine and deep learning based regressors, proposed meta-regressor along with optimal subset of feature/s achieves the best R 2 score of 98% for 6 regions and 97% for other 3 regions of Pakistan. MPF-Net as web service is accessible here. • Deep impact analysis of different features for optimal solar irradiance forecasting. • Finding an appropriate combination of feature selection algorithms and Regressors. • Rich performance comparison of diverse machine and deep learning methodologies. • Development of a meta-regressor that reaps the benefits of different regressors. • Public web-interface and API with multiple feature selection approaches and regressors. [ABSTRACT FROM AUTHOR]

The technological evolution of the smart grids is going to take its shape in the form of a new paradigm called the Internet of Energy (IoE); which is considered to be the convergence of internet, communication, and energy. Like other evolved technologies, the IoE inherits security vulnerabilities from its constituents that need to be addressed. Intrusion Detection Systems (IDS) have been used to counteract malicious attacks. Among the types of IDS, anomaly-based IDS that employ mostly machine learning algorithms are considered to be the promising one, owing to their capability of detecting zero-day attacks. However, using complex algorithms to detect attacks, the existing anomaly-based IDS designed for IoE require considerable amount of time. It is tempting to reduce the training and testing time in order to make the IDS feasible for the IoE architecture. In this paper, we propose a hybrid anomaly-based IDS that can be installed at any networked site of the IoE architecture, such as Advanced Metering Infrastructure (AMI), to counteract security attacks. Our proposed system reduces the overall classification time of detection compared to the existing hybrid methods. The proposed solution uses a combination of K-means and Support Vector Machine, where the K-means centroids are used in a unique training method that reduces the training and testing times of the Support Vector Machine without compromising classification performance. We choose the best value of "k" and fine-tuned the SVM for best anomaly detection. Our approach achieves the highest accuracy of 99.9% in comparison with the existing approaches. • A hybrid anomaly-based IDS is proposed for the Internet of Energy architecture. • Our approach achieves 99.9% accuracy as compared to the existing approaches. • Our approach performs 1367 times faster than the existing approaches. [ABSTRACT FROM AUTHOR]

Load forecasting avoids energy wastage by accurately estimating the future quantity of energy generation and demand. Existing load forecasting approaches do not utilize the potential of feature selection and dimensionality reduction approaches that remove irrelevant/redundant features and improve the performance of machine learning (ML) regressors. This research presents an end-to-end framework named Energy Generation and Demand forecasting Search Net (EGD-SNet) capable of predicting energy generation, demand and temperature in multiple regions. EGD-SNet framework contains 13 different feature selection and 11 dimensionality reduction algorithms along with 10 most widely used ML regressors. It makes use of Particle Swarm Optimizer (PSO) to smartly train regressors by finding optimal hyperparameters. Further, it has potential to design an end to end pipeline by finding appropriate combination of regressor and feature selection or dimensionality reduction approaches for precisely predicting energy generation or demand for a particular regional data based on the characteristics of data. EGD-SNet as web service is accessible here. http://111.68.102.19:8000/ • Performance analysis of 11 dimensionality reduction methods. • Deep performance exploration of 13 different feature selection approaches. • Performance comparison of 10 machine learning regressors. • Particle swarm optimizer potential exploration to find optimal hyperparameters. • Development of computational framework capable of designing optimal load forecasting pipelines. [ABSTRACT FROM AUTHOR]

Forecasting electricity consumption can help policymakers to properly plan for economic development. This is possible through energy conservation by avoiding excessive consumption of electricity through enhanced operational strategy. Power utilization and financial improvement are in long term relationship with all member nations of the Organization of Petroleum Exporting Countries (OPEC). In order to improve electricity consumption forecasting performance, this paper proposes an alternate machine learning method for forecasting OPEC electricity consumption with improved performance. The modeling of the OPEC electricity utilization forecast depends on the Cuckoo Search Algorithm by means of Lévy flights. The proposed method is found to be efficient, operative, consistent, and robust compared to the electricity consumption forecasting methods that have already been discussed by researchers in the literature. In turn, energy conservation can be motivated in the twelve OPEC member countries. [ABSTRACT FROM AUTHOR]