Undesirable Data Research Articles

Quilt: Robust Data Segment Selection against Concept Drifts

Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data streams where the joint distribution of the data X and label y, P(X, y), changes over time and possibly degrade model accuracy. Existing concept drift adaptation approaches mostly focus on updating the model to the new data possibly using ensemble techniques of previous models and tend to discard the drifted historical data. However, we contend that explicitly utilizing the drifted data together leads to much better model accuracy and propose Quilt, a data-centric framework for identifying and selecting data segments that maximize model accuracy. To address the potential downside of efficiency, Quilt extends existing data subset selection techniques, which can be used to reduce the training data without compromising model accuracy. These techniques cannot be used as is because they only assume virtual drifts where the posterior probabilities P(y|X) are assumed not to change. In contrast, a key challenge in our setup is to also discard undesirable data segments with concept drifts. Quilt thus discards drifted data segments and selects data segment subsets holistically for accurate and efficient model training. The two operations use gradient-based scores, which have little computation overhead. In our experiments, we show that Quilt outperforms state-of-the-art drift adaptation and data selection baselines on synthetic and real datasets.

Merging decision-making units in the simultaneous presence of desirable and undesirable factors

This paper is devoted to applying the inverse Data Envelopment Analysis (InvDEA) in the simultaneous presence of desirable and undesirable factors. One of the most common ways to improve units’ performance in the business environment is through activity synergies called units’ merging. The present study models how to identify the inherited input/output from the units participating in the merger process to achieve the desired efficiency goal. The proposed models are established based on the InvDEA approach and multiple-objective programming tools. Sufficient conditions to estimate desirable and undesirable data are obtained using Pareto solutions to multi-objective programming problems. The theory extended in the study is explained by an application in the banking sector.

Artificial Intelligence-Based Prediction of Renewable Energy Sources Using Correlation Testing

The purpose of renewable energy to generate power and change the composition of the energy mix is becoming more popular in today’s world. The competition of solar power plants in the energy market and decreasing reliance on fossil fuels for socio andeconomic growth are both facilitated by solar energy forecasting, which is a critical component. To overcome this issue, we presented an Adaptive sea lion optimized genetic adversarial network (ASLO-GAN) method. The purpose of the ASLO-GAN method is to predict the renewable energy sources. The dataset of renewable energy sources (RESs) is collected from kaggleand then the collected dataset is preprocessed using decimal scale normalization. When extracting the data, the spearman rank-order correlation (SROC) method is utilized so undesirable data can be omitted from the process. The simulation results include RMSE, MAPE, R-square, CV(RMSE) and NMBE that are evaluated. The main key findings of NMBE were compared to other traditional approaches in order to show the validity and repeatability of the inquiry.

Efficiency decomposition in three-stage network with fuzzy desirable and undesirable output and fuzzy input in data envelopment analysis

PurposeThe purpose of this paper is to improve the recent models for the evaluation of the efficiency of decision making units (DMUs) comprising a network structure with undesirable intermediate measures and fuzzy data.Design/methodology/approachIn this paper a three-stage network structure model with desirable and undesirable data is presented and is solved as linear triangular fuzzy planning problems.FindingsA new three stage network data envelopment analysis (DEA) model is established to evaluate the efficiency of industries with undesirable and desirable indicators in fuzzy environment.Practical implicationsThe implication of this study is to evaluate the furniture services and the chipboard industries of wood lumber as a three-stage process.Originality/valueIn some cases, DMUs include two or multi-stage process (series or parallel) operating with a structure called a network DEA. Also, in the real world problems, the data are often presented imprecisely. Additionally, the intermediate measures under the real-world conditions include desirable and undesirable data. These mentioned indexes show the value of the proposed model.

Ranking of Efficient and Non-Efficient Decision Making Units with Undesirable Data Based on Combined Models of DEA and TOPSIS

Ranking of Efficient and Non-Efficient Decision Making Units with Undesirable Data Based on Combined Models of DEA and TOPSIS

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.

Sequential Malmquist-Luenberger productivity index for interval data envelopment analysis

<p style='text-indent:20px;'>Data envelopment analysis (DEA) based productivity indexes models are widely applied to evaluate the productivity of decision-making units over a period. This study proposes a productivity index for evaluating environmentally sensitive productivity growth while excluding the possibility of spurious technical regress. This innovative index has been created by combining directional distance functions, sequential DEA, undesirable data, and the concept of interval DEA. With this combination, the traditional sequential Malmquist-Luenberger productivity index (SMLPI) has been reformulated as an interval DEA problem to present a novel productivity index named interval SMLPI. We propose a decomposition of the developed index utilizing both constant returns to scale and variable returns to scale frontiers as the benchmark, which allows us to quantify scale efficiency change with interval data. To exhibit the capability of the proposed extension of SMLPI, we model a framework for Indian commercial banks and measure productivity change intervals for twenty-one banks from 2011 to 2018. The empirical findings elucidate that the scale efficiency change plays an essential role in driving productivity change. ICICI Bank had the highest average marginal productivity gain of 1.5007 between 2011 and 2018, whereas Karur Vysya bank had the highest average marginal productivity decline of 0.9411.</p>

ISort: SSD Internal Sorting Algorithm for Big Data

As a basic algorithm for big data processing, external sorting suffers from massive read and write operations in the external memory. Recent works separate part of the data processing work from the host side to the solid state drive (SSD) to reduce data transmission. However, the internal memory of the SSD is limited, and undesirable data retention could occur during the merge phase. Therefore, to improve the efficiency of memory, we propose an algorithm named ISort. Specifically, we build an index table between the memory and the address. The index table determines the order of pages being read in the merge phase according to their minimum values, which are read into memory sequentially to reduce the data residing in memory and improve memory efficiency. Since the merge phase is performed inside the SSD, ISort can take advantage of the high IO bandwidth within the SSD to speed up the execution of the merge phase. We search for the optimal ratio of read and write channels by comparing the “specialized channel” and the “hybrid channel” for data of read and write performance because the utilization of the channel will directly influence performance. Experimental results show that ISort can maintain better data processing speed when SSD memory is limited, outperforming other robust algorithms. In addition, the algorithm’s performance using the crossover strategy is better than that using the specialization strategy.

A real-time machine learning application for browser extension security monitoring

ABSTRACT One of the most common attacks in browser extensions is Cross-site scripting (XSS). To address these challenges, several browsers have proposed a new mechanism where legitimate browser extensions can only be installed from their respective Web Stores. Nonetheless, this mechanism is not flawless and multiple users still choose to install browser extensions from other sources, leaving them exposed to multiple types of attacks. This paper proposes a browser extension capable of detecting XSS attacks using Machine Learning (ML), as well as other irregularities that may occur in recently installed browser extensions. Regarding the detection of XSS attacks, the detection model is based on the Support Vector Machine (SVM) and it was able to detect malicious scripts with an accuracy of 99.5%, a precision of 99.4%, and a recall of 99.0%. Additionally, the detection of two other types of irregularities, namely the presence of blacklisted or irregular URLs located in the browser extension, and the presence of undesirable data in the manifest file of the browser extension, were considered. A Windows application was also designed in Java and deployed alongside the browser extension to monitor suspicious network requests from the newly installed browser extension.

Deep Learning Based Intrusion Prevention System in Vehicular Network

In the internet of vehicles, safety-based communication is carried out for prevention, mitigation, and alleviation of accidents through cooperative messages, position sharing, and the exchange of speed data between the vehicle (nodes) and corresponding roadside units. However, such networks are susceptible to false alarms and mispositioning of vehicles. It is therefore imperative to authenticate and identify normal messages from aggressive and incorrect messages. In this context, this paper has emphasized on a deep learning technique utilizing binary classification for segregating normal and malicious packets. The procedure is initiated by the preparation of training datasets from KDD99 and CICIDS 2018-type of open-source datasets having 1,20,223 packets and 41 features. An autoencoder is used in the preprocessing stage for the elimination of undesirable data right from the beginning. The 23 salient features are filtered out of 41. For training of the models, a structural deep neural network is utilized along with a Softmax classifier and rectified linear unit (ReLU) activation functions. The complete intrusion prevention (IP) mechanism is further trained & tested with Google co-labs as the open platform cloud service with open-source tensor flow. Furthermore, simulation data set developed in a network-simulating procedure is used for validation of the model. The results of the experimentation have established an accuracy 99.57% greater than existing recurrent neural network and convolution Neural Network models. For work in future, various datasets can be used for training to improve the accuracy and efficacy.

Transition to Low-Carbon Hydrogen Energy System in the UAE: Sector Efficiency and Hydrogen Energy Production Efficiency Analysis

To provide an effective energy transition, hydrogen is required to decarbonize the hard-to-abate industries. As a case study, this paper provides a holistic view of the hydrogen energy transition in the United Arab Emirates (UAE). By utilizing the directional distance function undesirable data envelopment analysis model, the energy, economic, and environmental efficiency of UAE sectors are estimated from 2001 to 2020 to prioritize hydrogen sector coupling. Green hydrogen production efficiency is analyzed from 2020 to 2050. The UAE should prioritize the industry and transportation sectors, with average efficiency scores of 0.7 and 0.74. The decomposition of efficiency into pure technical efficiency and scale efficiency suggests policies and strategies should target upscaling the UAE’s low-carbon hydrogen production capacity to expedite short-term and overall production efficiency. The findings of this study can guide strategies and policies for the UAE’s low-carbon hydrogen transition. A framework is developed based on the findings of the study.

Wildlife Wrangler: A high-level data processing framework that supports the utilization of species occurrence data for biogeographical analyses

Wildlife Wrangler: A high-level data processing framework that supports the utilization of species occurrence data for biogeographical analyses

A novel deep neural network based marine predator model for effective classification of big data from social Internet of Things

SummarySocial Internet of Things (SIoT) is considered as one of the most recently evolved topics that connects people and object, object and object as well as people and people. The SIoT and big data provide an exact representation of IoT and social system for human progression characterization. Numerous machine learning techniques are employed to classify the data gathered from SIoT in a more powerful way. In this article, a deep neural network based marine predator (DRNN‐MP) algorithm is proposed in classifying big data. Here, an adaptive Savitzky–Golay filter is employed for selecting the subset and to eliminate undesirable data, as well as different noises. The big data databases are reduced using a Hadoop map reducing framework thereby enhancing the performances of the proposed approach. In addition to this, a modified relief technique is employed to select optimal features thereby performing better classification. The testing and training process based on the proposed approach for optimal classification of features from the big data considers four different databases namely coronary illness, GPS trajectories, localization data, and water treatment plant obtained from UCI machine learning repository. In addition to this, the proposed approach is evaluated for diverse performance measures namely accuracy, precision, specificity, sensitivity, throughput, and energy consumption. Finally, the proposed approach is compared for various metrics to illustrate the effectiveness of the system and the results demonstrated that the accuracy of our work is 98.25%.

CO2, Environmental Emergencies, and Industrial Pollution Assessment in China from the Perspective of the Circular Economy

The rise of energy consumption has also increased emissions of the “three wastes” (wastewater, waste gas, and industrial solid waste), and environmental emergencies caused by pollutants, natural disasters, and production safety accidents have aroused social concerns. As few scholars have combined treatment efficiency of the three wastes with environmental emergencies to explore their relationships, this research thus uses a two-stage undesirable Data Envelopment Analysis (DEA) method to explore the situations of 4 regions and 30 provinces in China from 2013 to 2017 based on such interactive perspectives. The study finds that the overall regional environmental efficiency in China is generally low, and in terms of regional differences, the eastern and northeastern regions are better than the central and western regions. The efficiency values of the three wastes in China have also fluctuated greatly from 0.7 down to 0.2 in recent years. The efficiency of environmental emergencies in China is greatly impacted by the efficiency of environmental governance inputs. Based on the results, the study proposes that the eastern provinces can be an example for promoting balanced regional development and offers policy recommendations such as taking precautions against environmental emergencies.

A New Robust Network Slack Based Measure Model in the Presence of Negative and Undesirable Data

A New Robust Network Slack Based Measure Model in the Presence of Negative and Undesirable Data

Outlier-Resistant Filtering With Dead-Zone-Like Censoring Under Try-Once-Discard Protocol

In this paper, a novel outlier-resistant filtering problem is concerned for a class of networked systems with dead-zone-like censoring under the weighted try-once-discard protocol (WTODP). To describe the phenomenon of dead-zone-like censoring, the sensor output is characterized by the Tobit model in which the censored region is restrained by specified left- and right-censoring thresholds. The WTODP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. In the case of the measurement outliers, a saturation function is employed in the Tobit Kalman filter structure to constrain the innovations contaminated by the measurement outliers, thereby maintaining satisfactory filtering performance. By resorting to the approach of the matrix inequality, an upper bound is first obtained on the filtering error covariance where the gain matrix of the Tobit Kalman filter is carefully designed to minimize the obtained upper bound. Moreover, the exponential boundedness of the filtering error is analyzed in the mean square sense. Finally, the effectiveness of the proposed outlier-resistant filtering algorithm is verified by three practical examples.

Finite-Horizon H-Infinity Control for Time-Varying State-Saturated Systems under Stochastic Communication Protocol

In this paper, the H∞ control problem is investigated for a class of time-varying state-saturated systems with both stochastic parameters and nonlinearities under the stochastic communication protocol (SCP). The sensor-to-controller network is considered where only one sensor can obtain access to the communication network at each transmission instant. The SCP is adopted to mitigate the undesirable data collision phenomenon. The model transformation technique is employed to simplify the addressed problem, and then, the completing squares method is carried out to obtain a sufficient condition for the existence of the finite-horizon H∞ controller. The controller parameters are characterized by solving two coupled backward recursive Riccati-like difference equations. A simulation example is finally utilized to illustrate the effectiveness of the proposed controller design scheme.

Evaluating Efficiency in Construction Projects with the TOPSIS Model and NDEA Method Considering Environmental Effects and Undesirable Data

Evaluating Efficiency in Construction Projects with the TOPSIS Model and NDEA Method Considering Environmental Effects and Undesirable Data

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

AbstractIn this article, the Tobit Kalman filtering problem is investigated for a class of discrete time‐varying fractional‐order systems in the presence of measurement censoring and stochastic nonlinearities under the Round‐Robin protocol (RRP). The fractional‐order dynamic model is described by the Grunwald–Letnikov difference equation, and the statistical means are utilized to characterize the stochastic nonlinearities that include state‐dependent stochastic disturbances as a special case. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. Under the RRP scheduling, only one sensor is permitted to transmit its measurement over the network at each time instant. In light of the renowned orthogonality projection principle, a protocol‐based fractional Tobit Kalman filter is devised with the fractional dynamics and stochastic nonlinearities elaborately addressed. In the pursuit of the filter design, a couple of new terms appear which are in relation to the RRP, fractional dynamics and stochastic nonlinearities arise, and these terms are adequately handled recursively or off‐line. Simulation results are provided to demonstrate the usefulness of the proposed method.

Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

The evolution of Industry 4.0 made it essential to adopt the Internet of Things (IoT) and Cloud Computing (CC) technologies to perform activities in the new age of manufacturing. These technologies enable collecting, storing, and retrieving essential information from the manufacturing stage. Data collected at sites are shared with others where execution automatedly occurs. The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process. However, information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern. The current research validates the information optimally to offer a minimum set of activities to complete the disassembly process. An optimal disassembly sequence plan (DSP) can possess valid information to organize the necessary actions in manufacturing. However, finding an optimal DSP is complex because of its combinatorial nature. The genetic algorithm (GA) is a widely preferred artificial intelligence (AI) algorithm to obtain a near-optimal solution for the DSP problem. The converging nature at local optima is a limitation in the traditional GA. This study improvised the GA workability by integrating with the proposed priori crossover operator. An optimality function is defined to reduce disassembly effort by considering directional changes as parameters. The enhanced GA method is tested on a real-time product to evaluate the performance. The obtained results reveal that diversity control depends on the operators employed in the disassembly attributes. The proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the product. The effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.