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Modern tools for natural language generation may enable novel forms of scholarly fraud based on the automatic generation of fake review reports for academic papers, i.e., of a few sentences broadly related to the textual content of a submission and written with the style of an anonymous reviewer. A tool capable of generating such reports automatically and for free could enable various forms of unethical behavior by publishers and researchers. In this work we experiment with a simple heuristic that makes use of widely available and easy to use tools for natural language generation, including the Generative Pretrained Transformer 2 (GPT-2), in order to craft fake reviews automatically. We also perform a small user study for assessing the credibility of those reviews. Our analysis suggests that academic frauds based on fake reviews may indeed be feasible and ready to be deployed in the wild.

Intelligent systems on Paper currency recognition and verification are inevitable for modern banking services. These systems are used in Auto-seller machines, vending machines etc. Extracting sufficient and reliable monetary characteristics are essential for accuracy and performance of such systems. This paper proposes a new intelligent system for paper currency recognition. Pakistani paper currency has been considered, as a case study, for intelligent recognition. This paper identifies, introduces, and extracts robust features from Pakistani banknotes. After extracting these features, the paper proposes to use three layers feed-forward Backpropagation Neural Network (BPN) for intelligent classification. The proposed technique and system are simple and comparatively less time consuming which makes it suitable for real-time applications. In order to evaluate the performance of the proposed technique, experiments have been conducted on 175 Pakistani banknotes. The results indicate that system has 100% recognition ability on properly captured images.

The purpose of this paper is to present a hybridized technique for analyzing the behavior of an industrial system stochastically by utilizing vague, imprecise, and uncertain data. In the present study two important tools namely Lambda-Tau methodology and particle swarm optimization are used to formulate the hybridized technique PSOBLT Particle swarm optimization based Lambda-Tau for analyzing the behavior of the complex industrial system stochastically up to a desired degree of accuracy. Expressions of reliability indices like failure rate, repair time, mean time between failures MTBF, expected number of failures ENOF, reliability and availability for the system are obtained by using Lambda-Tau methodology and particle swarm optimization is used to construct the membership function. Fault tree is used to model the system. The press unit of a paper mill situated in a northern part of India, producing approximately 200 tons of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of a system's behavior has also been done. The behavior analysis results computed by PSOBLT technique have a reduced region of prediction in comparison of existing Lambda-Tau and GABLT Genetic algorithm based Lambda-Tau technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. Thus the paper suggests an approach to improve the systems' performance.

Image authentication is an important research topic of maintaining the integrity of digital image contents. Fragile image authentication is the technique for achieving the goal of image content integrity maintenance. This article presents a fragile image authentication scheme based on the concept of wet paper codes. The proposed scheme modifies dry pixels on an image to conceal an image signature. The proposed authentication scheme can exactly detect the tampered area on a tampered image. For saving computation cost of signature embedding, an exclusive-or operation is used in the proposed authentication scheme. The experimental results show that the proposedmethod not only has good visual quality of an authorized image but also successfully detects tampered areas on a tampered image.

The papers in this issue are a selection of the papers presented at the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES2012 held on 10, 11 and 12 September 2012, in San Sebastian, Spain. The main bias for the selection of the papers has been the proposition of foundational works or reviews that focus on some specific issues of intelligent systems and knowledge engineering. The variety of the papers collected is great going from some abstract mathematical topics up to more close to the earth applications of knowledge engineering such as information retrieval.

The papers in this issue are a selection of the papers presented at the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems KES2012 held on 10, 11 & 12 September 2012, in San Sebastian, Spain. The main bias for the selection of the papers has been the use of ontologies for knowledge modeling and their applications. There have been contributions ranging in all aspects of ontology processing, construction and application which shows the vitality of this field, one of the most ready to advance in computer science.

In the new era of computational intelligence, the ever-expanding abundance of information storage and processing power enables researchers and users to tackle applications on various scientific domains of Decision Support Systems DSS. The general focus of this special issue is to provide insights on how computational intelligence methodologies and algorithms can be employed in real world applications, so as to produce information systems capable of solving important and hard decision problems. This special issue on "Innovative Decision Systems" contains extended versions of seven 7 papers selected from the 12th EANN/7th AIAI 2011 Joint Conference. The manuscripts were accepted for publication, after passing through a peer review process by at least two independent academic referees. AIAI and EANN are two well-established annual events, technically sponsored by the Technical Committee 12, Working Group 12.5 TC12-WG12.5 of the International Federation for Information Processing IFIP and the International Neural Network Society INNS respectively. The 2011 event was held on September 15--18 2011 in Corfu, Greece.

The Eighth International Conference on Knowledge Based Intelligent Information and Engineering Systems was held at the Intercontinental Hotel, Wellington, hosted by Wellington Institute of Technology, New Zealand in September 2004. KES2004 aimed to provide a high-tech forum for the presentation of recent research into the theory and applications of intelligent systems and techniques. However, it also focused on some significant emerging intelligent technologies including evolvable hardware, evolutionary computation in computational intelligence, DNA computing, artificial immune systems, bioinformatics using intelligent and machine learning techniques, and intelligent web mining. The conference attracted about 500 delegates from 55 countries and the proceedings contained approximately 500 papers. This Special Issue contains extended versions of nine papers presented at KES2004, selected for qualities of innovation, application of leading-edge intelligent techniques, or overall excellent quality research. The first paper,by A. Kusiak, A. Burns and F. Milster, describes a data-mining approach applied to the analysis of parameters relating to a circulating fluidisedbed boiler. The outcome of the research has interesting implications on the direction of research into the optimisation of energy production. The second paper, by B. Kostek and J. Wojcik, describes innovative work in which techniques often used in data-mining were applied to improve the effectiveness of the retrieval of stored musical rhythms. The next paper, by J.A. Rose, describes work relating to recent developments in DNA computing. Theory and results are presented. The fourth paper, by M.F. Ursu, B. Virginas, G. Owusu and C. Voudouris, describes an approach to workforce allocation, modelled as a distributed system. The work combines an agent-based model combined with rulebased expressions in an original combination. Good global solutions are obtained from the distributed algorithm. The fifth paper, by V.K. Murthy, describes research in which contextual knowledge management in peer to peer computing is applied to mobile-multiplayer games and robotics. Paper number six, by M. Ong, X. Ren, G. Allan, V. Kadirkamanathan, H.A. Thompson and P.J. Fleming, presents a practical framework under which to build a decision support system using a Grid computing paradigm. The system is applied to aeroengine monitoring. The next paper, authored by R. Ranawa, V. Palade and G.E.M.D.C. Bandara, describes an approach to the automatic generation of a fuzzy rule base for on-line hand-written alphanumeric character recognition. The method was found by the authors to be reliable and simple. The penultimate paper of the selection, written by D. Kim, N.-H. Kim, S.-J. Seo and G.-T. Park, describes simulation-based work that uses a fuzzy system to effectively model a practical walking bipedal robot. The final paper of the special issue, authored by V. Gorodetsky, O. Karsaev and V. Samoilov, describes an intelligent systems based generic approach to the on-line updating of situation assessment. We would like to thank the authors for informing us of the results of their work through their papers. We would also like to thank the reviewers for their comments, which resulted in improvements in the papers. We hope that readers appreciate from the papers some of the challenges of modern intelligent systems research, and some of the approaches that are being adopted to overcome them.

Tumor tracking is performed during patient set-up and monitoring of respiratory motion in radiotherapy. In the clinical setting, there are several types of equipment for this set-up such as the Electronic Portal imaging Device (EPID) and Cone Beam CT (CBCT). Technically, an optical positioning system tracks the difference between the infra ball reflected from body and machine isocenter. Our objective is to compare the clinical positioning error of patient setup between Cone Beam CT (CBCT) with the Optical Positioning System (OPS), and to evaluate the traditional positioning systems and OPS based on our proposed approach of patient positioning. In our experiments, a phantom was used, and we measured its setup errors in three directions. Specifically, the deviations in the left-to-right (LR), anterior-to-posterior (AP) and inferior-to-superior (IS) directions were measured by vernier caliper on a graph paper using the Varian Linear accelerator. Then, we verified the accuracy of OPS based on this experimental study. In order to verify the accuracy of phantom experiment, 40 patients were selected in our radiotherapy experiment. To illustrate the precise of optical positioning system, we designed clinical trials using EPID. From our radiotherapy procedure, we can conclude that OPS has higher precise than conventional positioning methods, and is a comparatively fast and efficient positioning method with respect to the CBCT guidance system.

Whilst common sense knowledge has been well researched in terms of intelligence and in particular artificial intelligence, specific, factual knowledge also plays a critical part in practice. When it comes to testing for intelligence, testing for factual knowledge is, in every-day life, frequently used as a front line tool. This paper presents new results which were the outcome of a series of practical Turing tests held on 23rd June 2012 at Bletchley Park, England. The focus of this paper is on the employment of specific knowledge testing by interrogators. Of interest are prejudiced assumptions made by interrogators as to what they believe should be widely known and subsequently the conclusions drawn if an entity does or does not appear to know a particular fact known to the interrogator. The paper is not at all about the performance of machines or hidden humans but rather the strategies based on assumptions of Turing test interrogators. Full, unedited transcripts from the tests are shown for the reader as working examples. As a result, it might be possible to draw critical conclusions with regard to the nature of human concepts of intelligence, in terms of the role played by specific, factual knowledge in our understanding of intelligence, whether this is exhibited by a human or a machine. This is specifically intended as a position paper, firstly by claiming that practicalising Turing's test is a useful exercise throwing light on how we humans think, and secondly, by taking a potentially controversial stance, because some interrogators adopt a solipsist questioning style of hidden entities with a view that it is a thinking intelligent human if it thinks like them and knows what they know. The paper is aimed at opening discussion with regard to the different aspects considered.

The purpose of the Symposium on Combinatorial Search (SoCS) is to promote the study and understanding of combinatorial search algorithms through the organization of scientific meetings, publications, tutorials, and other public scientific and educational activities. The most prominent among its activities is the Annual Symposium on Combinatorial Search that has been organized annually since 2008. This short paper introduces the most relevant accomplishments of the Fifth Annual Symposium (SoCS 2012), that was held in July 2012 in Niagara Falls, Canada.

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

In Dempster-Shafer theory, belief structure plays a key role, which provides a useful framework for information representation of uncertain variables. Basic Probability Assignment (BPA) is the most important component, which is difficult to be determined due to the uncertainty of information. Generally, there are two ways to get BPA of evidential theory: One is a subjective judgment of the expert’s experience, Interval Belief Structure (IBS) can solve the fuzziness and uncertainty of expert’s judgment. The other is an objective calculation by sampling existing data, in which BPA is viewed as the point estimate. Therefore, one of the contributions of this paper is that the definitions and theories of Confidential Interval Belief Structure (CIBS) is developed to describe BPA in Dempster-Shafer theory, which can give a range of population parameter values and contain more information to deal with the uncertainty and fuzziness of existing data. And then, based on evidential reasoning rule for counter-intuitive behavior, another contribution of this paper is that the extended evidential reasoning approach with CIBS is proposed to obtain the combined belief degree. The proposed method can be flexibly adjusted by appropriate errors and confidence levels, which is the main advantage. Finally, a case of sustainable operation of Shanghai rail transit system to verify the feasibility of proposed method and great performance of the extended method is shown.

Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms.

Data envelopment analysis (DEA) is widely used to evaluate the performance of a group of homogeneous decision making units (DMUs). Considering the uncertainty, interval DEA has been introduced to fit into more situations. In this paper, an interval efficiency method based on slacks-based measure is proposed to solve the uncertain problems in DEA. Firstly, the maximum and minimum efficiency values of the evaluated DMU are calculated by the furthest and closest distance from the evaluated DMU to the projection points on the Pareto-efficient frontier, respectively. Then, the AHP method is used for the full ranking of DMUs. The paper uses the pairwise comparison relationship between each pair of DMUs to construct the interval multiplicative preference relations (IMPRs) matrix. If the matrix does not meet the consistency condition, a method to obtain consistency IMPRs is introduced. According to the consistency judgment matrix, the full ranking of DMUs can be obtained. Finally, we apply our method to the performance evaluation of 12 tourist hotels in Taipei in 2019.

In recent years, the research on object detection has been intensified. A large number of object detection results are applied to our daily life, which greatly facilitates our work and life. In this paper, we propose a more effective object detection neural network model ENHANCE_YOLOV4. We studied the effects of several attention mechanisms on YOLOV4, and finally concluded that spatial attention mechanism had the best effect on YOLOV4. Therefore, based on previous studies, this paper introduces Dilated Convolution and one-by-one convolution into the spatial attention mechanism to expand the receptive field and combine channel information. Compared with CBAM and BAM, which are composed of spatial attention and channel attention, this improved spatial attention module reduces model parameters and improves detection capabilities. We built a new network model by embedding improved spatial attention module in the appropriate place in YOLOV4. And this paper proves that the detection accuracy of this network structure on the VOC data set is increased by 0.8%, and the detection accuracy on the coco data set is increased by 7%when the calculation performance is increased a little.

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output inaccurate predictions. Before DNNs are deployed, adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, the attack success rate, i.e., the transferability of adversarial examples, still needs to be improved. Based on image augmentation methods, this paper found that random transformation of image brightness can eliminate overfitting in the generation of adversarial examples and improve their transferability. In light of this phenomenon, this paper proposes an adversarial example generation method, which can be integrated with Fast Gradient Sign Method (FGSM)-related methods to build a more robust gradient-based attack and to generate adversarial examples with better transferability. Extensive experiments on the ImageNet dataset have demonstrated the effectiveness of the aforementioned method. Whether on normally or adversarially trained networks, our method has a higher success rate for black-box attacks than other attack methods based on data augmentation. It is hoped that this method can help evaluate and improve the robustness of models.

In this paper, we presents an apporch for real-world human face close-up images cartoonization. We use generative adversarial network combined with an attention mechanism to convert real-world face pictures and cartoon-style images as unpaired data sets. At present, the image-to-image translation model has been able to successfully transfer style and content. However, some problems still exist in the task of cartoonizing human faces:Hunman face has many details, and the content of the image is easy to lose details after the image is translated. the quality of the image generated by the model is defective. The model in this paper uses the generative adversarial network combined with the attention mechanism, and proposes a new generative adversarial network combined with the attention mechanism to deal with these problems. The channel attention mechanism is embedded between the upper and lower sampling layers of the generator network, to avoid increasing the complexity of the model while conveying the complete details of the underlying information. After comparing the experimental results of FID, PSNR, MSE three indicators and the size of the model parameters, the new model network proposed in this paper avoids the complexity of the model while achieving a good balance in the conversion task of style and content.

Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments.

PMU can directly measure positive sequence voltage, phase and system frequency. In this paper, the design and implementation for optimum placement of PMU in power system network (PSN) has been performed using 5 different intelligent approaches at an emulation platform. Different case studies based on IEEE 7, 14 and 30 bus system have been performed and analyzed. In the studies, PMU device is used for the measurement of voltage and current magnitude as well as its phase and its performance has been compared with measured real signals of PSN. PMU measurement gives the accurate results and reliability to PSN. But PMUs are not economical, so PSN operator needs to install a minimum number of PMU in PSN so that system should be fully observable in a real-time scenario. In this paper for optimal placement of PMU, five different intelligent methods have been analyzed for three different bus systems and obtained results are compared. For the further validation of selected PMUs for the PSN, a state estimation using WLS algorithm has been performed using conventional data and PMU data on IEEE14 and IEEE30 bus systems. The obtained results for voltage estimation error and phase estimation error with and without PMU data are compared.

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.

For estimation of the RUL (Remaining useful life) of Lithium ion battery we are required to do its health assessment using online facilities. For identifying the health of a battery its internal resistance and storage capacity plays the major role. However the estimation of both these parameters is not an easy job and requires lot of computational work to be done. So to overcome this constraint an easy alternate way is simulated in the paper through which we can estimate the RUL. For formation of a linear relationship between health index of the battery (HI) and its actual capacity used of power transformation method is done and later on to validate the result a comparison study is done with Pearson & Spearman methods. Transformed value of Health Index is used for developing a neural network. The results demonstrated in the paper shows the feasibility of the proposed technique resulting in great saving of time

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.

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.

This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers.

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.

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done.

Digitalization is the key trend of the Industry 4.0 revolution. Industrial companies are transforming the way they design and maintain their products and solutions. The user requirements become more demanding. Competition among the manufacturing companies is at its limits and transforms the products to be more complex. Yet, other challenges such as faster time to market, higher quality requirements and legislation force enterprises to provide new ways of design, manufacture and service their end products. Product Lifecycle Management (PLM) is a key solution to track the entire lifespan of the product from idea to design, design to manufacture and manufacture to service. Besides the complexity of products and production, the selection of the right PLM solution which will become the backbone of enterprises is an open problem. In this paper, a thorough literature review is conducted to analyze the most important features for selecting the right PLM solution for manufacturing firms. Moreover, to overcome the challenge of decision makers’ (DM) subjective judgments, a novel interval value spherical fuzzy COPRAS (IVSF-COPRAS) multi-criteria decision making (MCDM) method is introduced. The paper aims to help enterprises rapidly identify the best alternative vendor/solution to be selected based on the need of the organization. In order to show the applicability, DM inputs are collected from a leading defense company where the PLM selection process is ongoing. The industrial case study is provided to demonstrate the success of the proposed selection framework.

In addition to the well-known fuzzy sets, a novel type of fuzzy set called spherical fuzzy set (SFS) is recently introduced in the literature. SFS is the generalized structure over existing structures of fuzzy sets (intuitionistic fuzzy sets-IFS, Pythagorean fuzzy sets-PFS, and neutrosophic fuzzy sets-NFS) based on three dimensions (truth, falsehood, and indeterminacy) to provide a wider choice for decision-makers (DMs). Although the SFS has been introduced recently, the topic attracts the attention of academicians at a remarkable rate. This study is the expanded version of the authors’ earlier study by Ozceylan et al. [1]. A comprehensive literature review of recent and state-of-the-art papers is studied to draw a framework of the past and to shed light on future directions. Therefore, a systematic review methodology that contains bibliometric and descriptive analysis is followed in this study. 104 scientific papers including SFS in their titles, abstracts and keywords are reviewed. The papers are then analyzed and categorized based on titles, abstracts, and keywords to construct a useful foundation of past research. Finally, trends and gaps in the literature are identified to clarify and to suggest future research opportunities in the fuzzy logic area.