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The availability of techniques for driver distraction detection has been difficult to put to use because of delays caused due to lag in inferencing the model. Distractions caused due to handheld devices have been major causes of traffic accidents as they affect the decision-making capabilities of the driver and gives them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents, which can easily be avoided if they had been attentive. As such, problems related to the driver's negligence towards safety a possible solution is to monitor the driver and driving behavior and alerting them if they are distracted. In this paper, we propose a novel approach for detecting when a driver is distracted due to in hand electronic devices which is not only able to detect the distraction with high accuracy but also is energy and memory efficient. Our proposed compressed neural got an accuracy of 0.83 in comparison to 0.86 of heavyweight network. [ABSTRACT FROM AUTHOR]

The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty-two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation-based performance demonstration and finally conclusive summary of the developed tool/technique along with the future scope. [ABSTRACT FROM AUTHOR]

Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics. [ABSTRACT FROM AUTHOR]

A convolution neural network (CNN) based deep learning method has been proposed for automatic classification and localization of nonlinear loads present in an interconnected power system. The identification of nonlinear loads has been previously dealt with the use of Nonlinear Auto Regression neural network with eXogenous inputs (NARX), Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and Fuzzy Logic (FL). However, these techniques had not explored the area of classification of industrial and domestic nonlinear loads in an interconnected power system. Also, a Deep learning-based solution for identification of the type of nonlinear load has not been reported in the literature to date. Hence, to address these shortcomings, an IEEE-9 Bus system with industrial nonlinear loads has been used to obtain various current waveforms with distortions. The recorded current waveforms are transformed into a time-frequency (TF) domain plane, and the obtained images are then fed to the deep learning algorithm. The colored images of the TF plots of each type of nonlinear load in Red-Green-Blue (RGB) index provide the best visual features for extraction. The TF domain signatures of individual events are scaled to a standard size before feeding to the algorithm. Through these TF signatures, unique features were extracted with the deep learning algorithm, and then passed on to different stages of convolution and max-pooling with fully connected layers. The softmax classifier at the end classifies the input data into the type of nonlinear present in the power system. The algorithm, when run at different buses, also identifies the location of the nonlinear load. The proposed methodology avoids the usage of any additional fusion layer for obtaining unique features, reduces the training time and maintains the highest accuracy of 100%. [ABSTRACT FROM AUTHOR]

It is the need of today's world, to deliver with quality health care services to meet the health needs of target populations. The healthcare system includes procedures of prevention and screening of all types of diseases, their treatment and diagnostics, recent research and development. These procedures must be maintained at a desired level of excellence, which comes under quality management. Quality management in healthcare incorporates with making of various quality policies, quality planning and assurance, quality control and quality improvement. Quality improvement (QI) is the scheme used for betterment of the services delivered to the patients, such as diagnosis and treatment. If these schemes are recent and advanced technology based, services provided would be cost effective, accurate, less time consuming and hassle-free for both healthcare provider as well as patients. In this study we are applying artificial intelligent and machine learning techniques to enhance the diagnosis accuracy of the liver fibrosis which is caused by hepatitis C virus (HCV). Generally, the SLBs (serial liver biopsies) are utilized to diagnose the liver fibrosis levels (LFLs), which is the gold standard method in this domain. However, SLB has various impediment and not appropriate to the patients which leads to higher prognosis cost with invasive way. So, there is a big research gap in the medical field to find out the alternative non-invasive approach/method for SLB. The proposed data-driven intelligent model for identification of liver fibrosis using hybrid approach is designed and implemented to overcome the SLBs problems with higher diagnostic accuracy. The empirical mode decomposition (EMD) approach is used to extract the IMFs (intrinsic mode functions), which are used as input features to the ANN-J48 algorithm based intelligent classifiers. The proposed approach shows the evidence for utilization in a non-invasive way to diagnose the LFLs without high level clinical expert skills. [ABSTRACT FROM AUTHOR]

Arrhythmia is an abnormality often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature's attributes were selected, and the final step was classifying data using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model. [ABSTRACT FROM AUTHOR]

With the increase in the amount of data available today, the responsibility of keeping that data safe has also taken a more severe form. To prevent confidential data from getting in the hands of an attacker, some measures need to be taken. Here comes the need for an effective system, which can classify the traffic as an attack or normal. Intrusion Detection Systems can do this work with perfection. Many machine learning algorithms are used to develop efficient IDS. These IDS provide remarkable results. However, ensemble-based IDS using voting have been seen to outperform individual approaches (Support Vector Machine and ExtraTree). Since the Voting methodology is able to work around both, theoretically similar and different classifiers and produce a single classifier based on the majority characteristics, it proved to be better than the other ensemble based techniques. In this paper, an ensemble IDS implementation is presented based on the voting ensemble method, using the two algorithms, Support Vector Machine (SVC) and ExtraTree. The experiment is performed on the KDDCup99 Dataset. The evaluation of the performance of the proposed method is based on the comparison with an unoptimized implementation of the same. The results based on performing the experiment in Python fetched an accuracy of 99.90%. [ABSTRACT FROM AUTHOR]

The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query. [ABSTRACT FROM AUTHOR]

Industrial Wireless Sensor Network (IWSN) includes numerous sensor nodes that collect data about target objects and transmit to sink nodes (SN). During data transmission among nodes, intrusion detection is carried to improve data security and privacy. Intrusion detection system (IDS) examines the network for intrusions based on user activities. Several works have been done in the field of intrusion detection and different measures are carried out to increase data security from the issues related to black hole, Sybil attack, Worm hole, identity replication attack and etc. In various existing approaches, secure data transmission is not achieved, therefore resulted in compromising the security and privacy of IWSNs. Accurate intrusion detection is still challenging task in terms of improving security and intrusion detection rate. In order to improve intrusion detection rate (IDR) with minimum time, generalized Frechet Hyperbolic Deep and Dirichlet Secured (FHD-DS) data communication model is introduced. At first, Frechet Hyperbolic Deep Traffic (FHDT) feature extraction method is designed to extract more relevant network activities and inherent traffic features. With the help of extracted features, anomalous or normal data is predicted. Followed by Statistical Dirichlet Anomaly-based Intrusion Detection model is applied to discover intrusion. Here, Dirichlet distribution is evaluated to attain secure data transmission and significantly detect intrusions in WSNs. Experimental evaluation is carried out with KDD cup 99 dataset on factors such as IDR, intrusion detection time (IDT) and data delivery rate (DDR). The observed results show that the generalized FHD-DS data communication method achieves higher IDR with minimum time. [ABSTRACT FROM AUTHOR]

Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA. [ABSTRACT FROM AUTHOR]

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), artificial neural network (ANN) and J48 algorithm of machine learning for real-time harmonics analysis of digital substation's equipment based on IEC-61850 using explanatory input variables based on laboratory proto-type real-time recorded database. In the proposed hybrid model, these variables are first extracted then diagnostic of power transformer harmonics of digital substation is evaluated/analyzed to perform the long term as well as the short term goal and planning in the electrical power network. In this paper, firstly, experimental analysis is performed to validate the laboratory prototype setup using FFT (fast Fourier transform), STFT (short-time Fourier transform) and CWT (continuous wavelet transform). Then, features are extracted from experimental dataset using EMD (empirical mode decomposition) method. The IMFs (intrinsic mode functions) have generated from EMD, which are used as an input variable to the two different diagnostic models, i.e., ANN and J48 algorithm. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using ANN and J48 method (with and without EMD method) and the results are compared. Obtained results shows that the proposed hybrid diagnostics approach for harmonics analysis has outperformance characteristics. [ABSTRACT FROM AUTHOR]

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices. [ABSTRACT FROM AUTHOR]

This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T-Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time. [ABSTRACT FROM AUTHOR]

Construction of robust regression learning models to fit training data corrupted by noise is an important and challenging research problem in machine learning. It is well-known that loss functions play an important role in reducing the effect of noise present in the input data. With the objective of obtaining a robust regression model, motivated by the link between the pinball loss and quantile regression, a novel squared pinball loss twin support vector machine for regression (SPTSVR) is proposed in this work. Further with the introduction of a regularization term, our proposed model solves a pair of strongly convex minimization problems having unique solutions by simple functional iterative method. Experiments were performed on synthetic datasets with different noise models and on real world datasets and those results were compared with support vector regression (SVR), least squares support vector regression (LS-SVR) and twin support vector regression (TSVR) methods. The comparative results clearly show that our proposed SPTSVR is an effective and a useful addition in the machine learning literature. [ABSTRACT FROM AUTHOR]

Weight initialization is the most important component which affects the performance of artificial neural network during training the network using Back-propagation algorithm. The initial starting weights have significant effect on the training. If the weights are too large then the sigmoid will saturate, that makes learning slow. If weights are too small then gradients are also too small. In this paper a new weight initialization method has been proposed. The results for the proposed weight initialization technique are compared against the random weight initialization method. In this paper the proposed weight initialization method is statistically analyzed. Ten different data sets out of which five sets of data are taken from UCI machine learning repository and five sets of data are generated using function approximation problems that are used. Resilient Back Propagation training algorithm is used for training the feed forward artificial neural network. The proposed weight initialization method gives better results when compared with random weight initialization technique. [ABSTRACT FROM AUTHOR]

Selection of suitable features plays a pivotal role in Electromyography pattern recognition (EMG-PR) based system designing. Time-domain features are widely used in EMG-PR based application and show improved proficiency in the development of rehabilitation robotics. Even though, the performance of existing features is not satisfactory. In this study, we proposed four novel time-domain features obtained by using first-order differentiation of original surface electromyogram (sEMG) signals feature. Here, sEMG signals were acquired from ten healthy volunteers with the help of myotrace400 device for six different arm movements. The data acquisition and pre-processing stage were carried out followed by the feature extraction process for better classification results. Four different classifiers namely, k-nearest neighbors (KNN), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Medium tree (MT) classifiers were utilized for the performance evaluation of proposed and conventional features. Experimental results demonstrate that proposed features extracted by using first-order differentiation of sEMG signals feature attained better classification accuracy with MT classifier as compared to the feature extracted from original sEMG signals with the conventional features. The accuracy of proposed feature based on first-order differentiation improved up to 6%. The results indicate that proposed features may be considered for developing the EMG-PR based system designing. [ABSTRACT FROM AUTHOR]

The requirement to manage complicated nonlinear systems with high uncertainty is one of the main drivers of progress in the identification and control discipline. Since most of the systems are nonlinear, exact identification and control of them using traditional techniques is a challenging task. This paper proposes a novel Takagi–Sugeno (T-S) type Recurrent Fuzzy System (RFS) for nonlinear system identification and adaptive control. To ensure the system's overall stability, a novel Lyapunov-stability-based learning algorithm is developed to obtain the parameter update equations for the proposed model. The performance of the proposed model is also evaluated with the well-known gradient-descent-based Back Propagation (BP) learning method and compared with the Lyapunov-stability method. To judge the efficacy of the proposed model and the learning algorithm we have performed an extensive comparative study by considering other popular soft-computing models such as Jordan Recurrent Neural Network (JRNN), Feed-Forward Neural Network (FFNN), Radial Basis Function Network (RBFN) and a conventional recurrent fuzzy system. The results obtained from the proposed method (RFS ＋ Lyapunov-stability-based learning algorithm) are compared with those obtained from other models and are found to be superior. • A novel recurrent fuzzy system is proposed which consists of internal feedback. • Parameter tuning algorithm is derived using the novel Lyapunov-stability principles. • Detailed comparative study is performed with state-of-the-art methods. • Proposed identification model requires only two inputs viz r (k) , y p k − 1 irrespective of the order of the plant. • RFS model-based controller is applied with only three inputs viz r (k) , y p k − 1 , y R (k − 1) irrespective of the order of the plant. [ABSTRACT FROM AUTHOR]

Abstract: Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control. [ABSTRACT FROM AUTHOR]