Journal of Intelligent & Fuzzy Systems - Volume 42, issue 6

Authors: Cheng, Ru | Wang, Lukun | Wei, Mingrun

Article Type: Research Article

Abstract: Finer-grained local features play a supplementary role in the description of pedestrian global features, and the combination of them has been an essential solution to improve discriminative performances in person re-identification (PReID) tasks. The existing part-based methods mostly extract representational semantic parts according to human visual habits or some prior knowledge and focus on spatial partition strategies but ignore the significant influence of channel information on PReID task. So, we proposed an end-to-end multi-branch network architecture (MCSN) jointing multi-level global fusion features, channel features and spatial features in this paper to better learn more diverse and discriminative pedestrian features. It …is worth noting that the effect of multi-level fusion features on the performance of the model is taken into account when extracting global features. In addition, to enhance the stability of model training and the generalization ability of the model, the BNNeck and the joint loss function strategy are applied to all vector representation branches. Extensive comparative evaluations are conducted on three mainstream image-based evaluation protocols, including Market-1501, DukeMTMC-ReID and MSMT17, to validate the advantages of our proposed model, which outperforms previous state-of-the-art in ReID tasks. Show more

Keywords: Person re-identification, multi-branch deep network, multi-level global fusion feature, spatial-channel partition

DOI: 10.3233/JIFS-212656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5987-6001, 2022

Authors: Qiu, Chenye | Liu, Ning

Article Type: Research Article

Abstract: This paper proposes a novel two layer differential evolutionary algorithm with multi-mutation strategy (TLDE) for solving the economic emission dispatch (EED) problem involving random wind power. In recent years, renewable energy such as wind power is more and more participated in the power systems to address the problems of fossil energy shortage and environmental pollution. Hence, the EED problem with the availability of random wind power is investigated in this paper. Due to the uncertain nature of wind speed, the Weibull probability distribution function is used to model the random wind power. In order to improve the search ability, TLDE …divides the population into two layers according to the fitness ranking, and individuals in the two layers are treated differently to fully investigate their own potential. The two layers can cooperate with each other to further enhance the search performance by utilizing an information sharing strategy. Also, an adaptive restart scheme is introduced to avoid falling into stagnation. The performance of the proposed TLDE is testified on the 40 units system with 2 modified wind turbines. The experimental results demonstrate that the TLDE method can achieve precise dispatch strategy in EED problem with random wind power. Show more

Keywords: Economic emission dispatch, wind power, differential evolution, mutation operator, two layer structure

DOI: 10.3233/JIFS-212735

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6003-6016, 2022

Authors: Wang, Zhi | Song, Shufang | Wei, Hongkui

Article Type: Research Article

Abstract: When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution of solution set simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed …for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils. Show more

Keywords: Teaching-learning-based optimization (TLBO), multi-objective optimization, convergence, distribution, airfoils

DOI: 10.3233/JIFS-212743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6017-6026, 2022

Authors: Shen, Yonghong

Article Type: Research Article

Abstract: In the present paper, the notion of the linearly correlated difference for linearly correlated fuzzy numbers is introduced. Especially, the linearly correlated difference and the generalized Hukuhara difference are coincident for interval numbers or even symmetric fuzzy numbers. Accordingly, an appropriate metric is induced by using the norm and the linearly correlated difference in the set of linearly correlated fuzzy numbers. Based on the symmetry of the basic fuzzy number, the linearly correlated derivative is proposed by the linearly correlated difference of linearly correlated fuzzy number-valued functions. In both non-symmetric and symmetric cases, the equivalent characterizations of the linearly correlated …differentiability of a linearly correlated fuzzy number-valued function are established, respectively. Moreover, it is shown that the linearly correlated derivative is consistent with the generalized Hukuhara derivative for interval-valued functions. Show more

Keywords: Fuzzy numbers, Linearly correlated difference, Linearly correlated derivative, Canonical form, Linearly correlated fuzzy number-valued functions

DOI: 10.3233/JIFS-212908

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6027-6043, 2022

Authors: Lu, Haishu | Li, Rong

Article Type: Research Article

Abstract: In this paper, based on the KKM method, we prove a new fuzzy fixed-point theorem in noncompact CAT(0) spaces. As applications of this fixed-point theorem, we obtain some existence theorems of fuzzy maximal element points. Finally, we utilize these fuzzy maximal element theorems to establish some new existence theorems of Nash equilibrium points for generalized fuzzy noncooperative games and fuzzy noncooperative qualitative games in noncompact CAT(0) spaces. The results obtained in this paper generalize and extend many known results in the existing literature.

Keywords: CAT(0) space, fuzzy fixed point, fuzzy maximal element, fuzzy noncooperative game, Nash equilibrium point

DOI: 10.3233/JIFS-212194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6045-6062, 2022

Authors: Sindhiya Devi, R. | Perumal, B. | Pallikonda Rajasekaran, M.

Article Type: Research Article

Abstract: In today’s world, Brain Tumor diagnosis plays a significant role in the field of Oncology. The earlier identification of brain tumors increases the compatibility of treatment of patients and offers an efficient diagnostic recommendation from medical practitioners. Nevertheless, accurate segmentation and feature extraction are the vital challenges in brain tumor diagnosis where the handling of higher resolution images increases the processing time of existing classifiers. In this paper, a new robust weighted hybrid fusion classifier has been proposed to identify and classify the tumefaction in the brain which is of the hybridized form of SVM, NB, and KNN (SNK) classifiers. …Primarily, the proposed methodology initiates the preprocessing technique such as adaptive fuzzy filtration and skull stripping in order to remove the noises as well as unwanted regions. Subsequently, an automated hybrid segmentation strategy can be carried out to acquire the initial segmentation results, and then their outcomes are compiled together using fusion rules to accurately localize the tumor region. Finally, a Hybrid SNK classifier is implemented in the proposed methodology for categorizing the type of tumefaction in the brain. The hybrid classifier has been compared with the existing state-of-the-art classifier which shows a higher accuracy result of 99.18% while distinguishing the benign and malignant tumors from brain Magnetic Resonance (MR) images. Show more

Keywords: Adaptive fuzzy filter, brain MR images, tumor diagnosis, hybrid classifier, segmentation

DOI: 10.3233/JIFS-212200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6063-6078, 2022

Authors: Cao, Yukun | Miao, Zeyu

Article Type: Research Article

Abstract: Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the …antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model’s overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE’s code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE . Show more

DOI: 10.3233/JIFS-212374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6079-6089, 2022

Authors: Gayathri, R. | Babitha Lincy, R.

Article Type: Research Article

Abstract: The paper describes the excellent method to get first-rate accuracy and performance in the discipline of Tamil character recognition in a handwritten mode. However, the subject is still at a nascent stage and grossly lacks adequate accuracy in the Tamil language, even though several studies have been conducted within the discipline of handwritten character recognition. This paper draws the attention to the offline handwritten recognition for the Tamil language using the Inception-v3 based transfer learning method. The proposed work is conducted on the readily available HP Tamil handwritten character offline dataset (Hewlett-Packard Lab: hpl-tamil-iso-char-offline-1.0.). It reveals that with the suitable …arrangement of transfer learning approach with Inception-v3, the pre-trained model can achieve the recognition accuracy of 93.1%, overtaking the former deep learning designs. The achieved accuracy is due to the use of a pre-trained version with transfer learning that regularly hastens the method of the training process on a new task. Overall, this results in higher accuracy and a more capable version. Show more

Keywords: Handwritten character recognition, inception-v3, tamil language, transfer learning

DOI: 10.3233/JIFS-212378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6091-6102, 2022

Authors: Vaishnavi, V. | Suveetha Dhanaselvam, P.

Article Type: Research Article

Abstract: The study of neonatal cry signals is always an interesting topic and still researcher works interminably to develop some module to predict the actual reason for the baby cry. It is really hard to predict the reason for their cry. The main focus of this paper is to develop a Dense Convolution Neural network (DCNN) to predict the cry. The target cry signal is categorized into five class based on their sound as “Eair”, “Eh”, “Neh”, “Heh” and “Owh”. Prediction of these signals helps in the detection of infant cry reason. The audio and speech features (AS Features) were exacted …using Mel-Bark frequency cepstral coefficient from the spectrogram cry signal and fed into DCNN network. The systematic DCNN architecture is modelled with modified activation layer to classify the cry signal. The cry signal is collected in different growth phase of the infants and tested in proposed DCNN architecture. The performance of the system is calculated through parameters accuracy, specificity and sensitivity are calculated. The output of proposed system yielded a balanced accuracy of 92.31%. The highest accuracy level 95.31%, highest specificity level 94.58% and highest sensitivity level 93% attain through proposed technique. From this study, it is concluded that the proposed technique is more efficient in detecting cry signal compared to the existing techniques. Show more

Keywords: Infant cry signal, spectrogram images, audio and speech features, mel-bark frequency cepstral domain, dense convolution neural network

DOI: 10.3233/JIFS-212473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6103-6116, 2022

Authors: Fan, Jianping | Zhou, Wei | Wu, Meiqin

Article Type: Research Article

Abstract: Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the …degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811. Show more

Keywords: Dempster-Shafer evidence theory, conflicting evidences, information fusion, Lance distance, non-extensive entropy

DOI: 10.3233/JIFS-212489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6117-6129, 2022

Authors: Lingaraj, Vanitha | Kaliannan, Kalaiselvi | Rohini, Venmathi Asirvatham | Thevasigamani, Rajesh Kumar | Chinnasamy, Karthikeyan | Durairaj, Vijendra Babu | Periasamy, Keerthika

Article Type: Research Article

Abstract: Flow state assessment is essential to understand the involvement of an individual in a particular task assigned. If there is no involvement in the task assigned then the individual in due course of time gets affected either by psychological or physiological illnesses. The National Crime Records Bureau (NCRB) statistics show that non-involvement in the task drive the individual to a depression state and subsequently attempt for suicide. Therefore, it is essential to determine the decrease in flow level at an earlier stage and take remedial steps to recover them. There are many invasive methods to determine the flow state, which …is not preferred and the commonly used non-invasive method is the questionnaire and interview method, which is the subjective and retroactive method, and hence chance to fake the result is more. Hence, the main objective of our work is to design an efficient flow level measurement system that measures flow in an objective method and also determines real-time flow classification. The accuracy of classification is achieved by designing an Expert Active k-Nearest Neighbour (EAkNN) which can classify the individual flow state towards the task assigned into nine states using non-invasive physiological Electrocardiogram (ECG) signals. The ECG parameters are obtained during the performance of FSCWT. Thus this work is a combination of psychological theory, physiological signals and machine learning concepts. The classifier is designed with a modified voting rule instead of the default majority voting rule, in which the contribution probability of nearest points to new data is considered. The dataset is divided into two sets, training dataset 75%and testing dataset 25%. The classifier is trained and tested with the dataset and the classification efficiency is 95%. Show more

Keywords: Stroop colour test, Flow Stroop Colour Word Test, expert active k-Nearest neighbour, flow state, electrocardiogram

DOI: 10.3233/JIFS-212504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6131-6144, 2022

Authors: Abdur Rahman, Usama | Jayakumar, C.

Article Type: Research Article

Abstract: Wireless visual sensor networks (WVSNs) have emerged as a strategic inter disciplinary category of WSN with its visual sensor based intelligence that has garnered considerable attention. The growing demand for energy efficient and maximized life time networks in highly critical applications like surveillance, military and medicine has opened up more prospects as well as challenges in the deployment of WVSNs. Multi-hop communication in WVSN results in overloading of intermediate sensor nodes due to its dual function in the network which results in hotspot effect. This can be mitigated with the help of mobile sinks and rendezvous points based route design. …But mobile sinks has to visit every cluster head to gather data which results in longer traversal path and higher latency and power consumption related issues if not addressed properly will impact the performance of the network. Our objective is to analyze and determine the optimal trajectory for mobile sink node traversal with the help of a high quality transmission architecture integrated with reinforcement learning and isolation forest based anomaly detection to propose an energy efficient meta-heuristic approach to enhance the performance of network by reducing the latency and securing the network against possible attacks. Show more

Keywords: Wireless visual sensor networks, mobile sinks, hotspot, reinforcement learning, isolation forest, anomaly detection, applications of WVSN

DOI: 10.3233/JIFS-212557

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6145-6157, 2022

Authors: Jayaseelan, Samuel Manoharan | Gopal, Sakthivel Thirumalai | Muthu, Sangeetha | Selvaraju, Sivamani | Patel, Md Saad

Article Type: Research Article

Abstract: Image enhancement is one of the most critical stages towards any image processing application. The outcome of image enhancement determines the accuracy and precise nature of the overall output from the image processing under interest. This research paper has shown specific interests towards enhancement of Scanned Electron Microscopic (SEM) images which are primarily concerned with projection of fine details exist in internal details of surfaces, metals, fine powders, fibers etc. These fine details play a dominant role in detection of minute cracks, artifacts, progressing faults, texture of powders, their coarseness or fineness, internal details of fibers in forensics. However, due …to the image capturing process which is through conventional camera-based models, noise tends to be a major source in degrading or blurring the underlying vital information. A cross neighbor fuzzy filter is a hybrid combination called Hybrid Fuzzy Based Cross Neighbor Filtering (HF-CNF) which is proposed in this research paper in order to minimize impulse and random noise to a great extent also to fine tune the further processing stages. The proposed method has been subjected to extensive analysis by comparison with state of art and recent benchmark methods and superior performance justified in terms of several validation metrics. Show more

Keywords: Image enhancement, scanned electronic microscopic, images, fuzzy filter, morphological processing, peak signal to noise ratio

DOI: 10.3233/JIFS-212561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6159-6169, 2022

Authors: Vijayanand, S. | Saravanan, S.

Article Type: Research Article

Abstract: Due to the growth of Big Data (BD) storage and access in cloud computing infrastructure, the detection of anomalies for Cloud Servers (CSs) is essential to ensure data confidentiality. Over the past decades, different security systems have been designed based on various methods like encryption, Access Policy (AP) control schemes, signcryption and so on. Among many security systems, a new Improved NTRU (INTRU) decryption based on the AP strategy has been suggested to secure the BD processed by the CSs. Also, the shared secret data was authenticated to defend the clients from anomalies in the cloud. But, the AP upgrade …must not degrade the confidentiality of storing information, reveal trust in the CS or cause any different security challenges. It is not considered that such security challenges occur when the data owner shares its data with many CSs. Hence in this article, an INTRU with Detecting Anomalous in CS (INTRU-DACS) system is proposed that employs a deep learning-based Anomaly Detection System (ADS) to handle and secure the BD stored in the CSs. The main goal of this method is to effectively identify the abnormalities in the real world by the conduct utilization, i.e., the System Call Identifier Sequences (SCISs) created from CSs in which these conducts are associated with BD. Initially, effective data summarization is constructed via different feature states to analyze the SCISs of specific durations. After that, an anomaly identification algorithm is proposed to train and test the streaming of raw SC sequences. This observable SCs execution task of CSs is gathered from log files. The variations of such SCISs having a specified duration are random for usual and unusual sequences. So, the fact of current normal and abnormal services is recognized regarding their SCISs. Such normal and abnormal behavioral states are learned from Convolutional Neural Network-Hidden Markov Model (CNNHMM) classifier to identify the anomalies in CSs. But, it is still a challenging process because of the patterns of usual and unusual events. The performance is not effective since it models only the conduct of a huge number of SCISs created from a single CS. As a result, a Secure Access Control Scheme with DACS (SACS-DACS) system is proposed in which a Multidimensional Feature Misbehavior Server Detection method (MFMSD) is introduced for detecting anomalies in multiple CSs. In this method, large-scale SCISs of multiple CSs are extracted, including different features such as network traffic sequence features, CPU energy usage and memory usage from host logs. These extracted multidimensional features are fed to the CNNHMM that identifies the anomalies and maximizes the detection accuracy. At last, the simulation results demonstrate the effectiveness of the SACS-DACS and INTRU-DACS as compared to the INTRU. Show more

Keywords: Big data, cloud computing, access control, improved NTRU, anomaly detection, CNN, HMM

DOI: 10.3233/JIFS-212572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6171-6181, 2022

Authors: Hamdi, Mohammed

Article Type: Research Article

Abstract: With the evaluation of the software industry, a huge number of software applications are designing, developing, and uploading to multiple online repositories. To find out the same type of category and resource utilization of applications, researchers must adopt manual working. To reduce their efforts, a solution has been proposed that works in two phases. In first phase, a semantic analysis-based keywords and variables identification process has been proposed. Based on the semantics, designed a dataset having two classes: one represents application type and the other corresponds to application keywords. Afterward, in second phase, input preprocessed dataset to manifold machine learning …techniques (Decision Table, Random Forest, OneR, Randomizable Filtered Classifier, Logistic model tree) and compute their performance based on TP Rate, FP Rate, Precision, Recall, F1-Score, MCC, ROC Area, PRC Area, and Accuracy (%). For evaluation purposes, We have used an R language library called latent semantic analysis for creating semantics, and the Weka tool is used for measuring the performance of algorithms. Results show that the random forest depicts the highest accuracy which is 99.3% due to its parametric function evaluation and less misclassification error. Show more

Keywords: Machine learning, software classification, software sustainability, data analytics

DOI: 10.3233/JIFS-212600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6183-6194, 2022

Authors: George, Sophia Jasmine | Ramaraju, Satish Kumar | Venkataraman, Vanitha | Kaliannan, Thenmalar | Kumaravel, Umadevi | Veerasundaram, M.

Article Type: Research Article

Abstract: Conventionally in many countries, electrical power industry is organized as vertically integrated system. Under this system, large utilities are authoritative for the generation, transmission and distribution of electrical power. Such utilities are governed by the rules and regulations of the government and are forced to operate within the prescribed guidelines with minimal profit. This confirmation causes an ineffective and sluggish perspective in power industry with a lack of technical innovation, competent management and customer satisfaction. To overcome these deficiencies, power sector around the globe is getting restructured. This paper addresses an inevitable technical disputes occurring in deregulated environment i.e., transmission …congestion which has an adverse effect on system security, increase in electricity pricing and line losses. Flexible AC Transmission System (FACTS) is a boon to the power sector which helps in a better and reliable power flow through the transmission lines. The problem is articulated as a multi objective function satisfying all the operational and security limits. Three heuristic algorithms namely Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS) and hybrid Quantum based PSO-Bio-geography based krill herd optimization (Q-PSOBBKH) algorithms were applied in finding solution to this complex congestion problem. To study the effectiveness of the proposed objective, IEEE 14 bus system was considered as the test system. In order to validate the proposed methodology three congestion cases i.e. bilateral transaction, multilateral transaction and overloading were imposed on the test bus system. Simulation was carried out in MATLAB. Show more

Keywords: Deregulated power system, particle swarm optimization, symbiotic organism search algorithm, hybrid quantum based PSO, bio-geography based Krill Herd Algorithm

DOI: 10.3233/JIFS-212717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6195-6208, 2022

Authors: Susmi, S. Jacophine

Article Type: Research Article

Abstract: Gene expression profiles are sequences of numbers, and the need to analyze them has now increased significantly. Gene expression data contain a large number of genes and models used for cancer classification. As the wealth of these data being produced, new prediction, classification and clustering techniques are applied to the analysis of the data. Although there are a number of proposed methods with good results, there is still limited diagnostics and a lot of problems still to be solved. To solve the difficulty, in this paper, an efficient gene expression data classification is proposed. To predict the cancer class of …patients from the gene expression profile, this paper presents a novel classification framework in the manner of three steps namely, Pre-processing, feature selection and classification. In pre-processing, missing value is filled and redundant data are removed. To attain the enhanced classification outcomes, the important features are selected from the database with the help of Adaptive Salp Swarm Optimization (ASSO) algorithm. Then, the selected features are given to the multi kernel SVM (MKSVM) to classify the gene expression data namely, BRCA, KIRC, COAD, LUAD and PRAD. The performance of proposed methodology is analyzed in terms of different metrics namely, accuracy, sensitivity and specificity. The performance of proposed methodology is 4.5% better than existing method in terms of accuracy. Show more

Keywords: Adaptive salp swarm optimization, gene expression data, multi kernel SVM, feature selection

DOI: 10.3233/JIFS-212733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6209-6220, 2022

Authors: Fernandes, Filipe | Stefenon, Stéfano Frizzo | Seman, Laio Oriel | Nied, Ademir | Ferreira, Fernanda Cristina Silva | Subtil, Maria Cristina Mazzetti | Klaar, Anne Carolina Rodrigues | Leithardt, Valderi Reis Quietinho

Article Type: Research Article

Abstract: The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the …contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models. Show more

Keywords: Long short-term memory, COVID-19, spreading viruses

DOI: 10.3233/JIFS-212788

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6221-6234, 2022

Authors: Davids, D. Minola | Christopher, C. Seldev

Article Type: Research Article

Abstract: The visual data attained from surveillance single-camera or multi-view camera networks is exponentially increasing every day. Identifying the important shots in the presented video which faithfully signify the original video is the major task in video summarization. For executing efficient video summarization of the surveillance systems, optimization algorithm like LFOB-COA is proposed in this paper. Data collection, pre-processing, deep feature extraction (FE), shot segmentation JSFCM, classification using Rectified Linear Unit activated BLSTM, and LFOB-COA are the proposed method’s five steps. Finally a post-processing step is utilized. For recognizing the proposed method’s effectiveness, the results are then contrasted with the existent …methods. Show more

Keywords: Video summarization, Levy Flight (LF) and opposition-based learning, Coyote Optimization Algorithm (LFOB-COA), Bi-directional Long Short-term Memory (BLSTM), Jaccard Similarity-centered Fuzzy C-Means (JSFCM)

DOI: 10.3233/JIFS-212800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6235-6243, 2022

Authors: Ajam, Leila | Nodehi, Ali | Mohamadi, Hosein

Article Type: Research Article

Abstract: Literature in recent years has introduced several studies conducted to solve the target coverage problem in wireless sensor networks (WSNs). Sensors are conventionally assumed as devices with only a single power level. However, real applications may involve sensors with multiple power levels (i.e., multiple sensing ranges each of which possesses a unique power consumption). Consequently, one of the key problems in WSNs is how to provide a full coverage on all targets distributed in a network containing sensors with multiple power levels and simultaneously prolong the network lifetime as much as possible. This problem is known as Maximum Network Lifetime …With Adjustable Ranges (MNLAR) and its NP-completeness has been already proved. To solve this problem, we proposed an efficient hybrid algorithm containing Genetic Algorithm (GA) and Tabu Search (TS) aiming at constructing cover sets that consist of sensors with appropriate sensing ranges to provide a desirable coverage for all the targets in the network. In our hybrid model, GA as a robust global searching algorithm is used for exploration purposes, while TS with its already-proved local searching ability is utilized for exploitation purposes. As a result, the proposed algorithm is capable of creating a balance between intensification and diversification. To solve the MNLR problem in an efficient way, the proposed model was also enriched with an effective encoding method, genetic operators, and neighboring structure. In the present paper, different experiments were performed for the purpose of evaluating how the proposed algorithm performs the tasks defined. The results clearly confirmed the superiority of the proposed algorithm over the greedy-based algorithm and learning automata-based algorithm in terms of extending the network lifetime. Moreover, it was found that the use of multiple power levels altogether caused the extension of the network lifetime. Show more

Keywords: Wireless sensor networks, cover set formation, scheduling algorithms, genetic algorithm, Tabu search

DOI: 10.3233/JIFS-202736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6245-6255, 2022

Authors: Pankajashan, Savaridassan | Maragatham, G. | Kirthiga Devi, T.

Article Type: Research Article

Abstract: Anomaly-based detection is coupled with recognizing the uncommon, to catch the unusual activity, and to find the strange action behind that activity. Anomaly-based detection has a wide scope of critical applications, from bank application security to regular sciences to medical systems to marketing apps. Anomaly-based detection adopted by various Machine Learning techniques is really a type of system that consists of artificial intelligence. With the ever-expanding volume and new sorts of information, for example, sensor information from an incontestably enormous amount of IoT devices and from network flow data from cloud computing, it is implicitly understood without surprise that there …is a developing enthusiasm for having the option to deal with more conclusions automatically by means of AI and ML applications. But with respect to anomaly detection, many applications of the scheme are simply the passion for detection. In this paper, Machine Learning (ML) techniques, namely the SVM, Isolation forest classifiers experimented and with reference to Deep Learning (DL) techniques, the proposed DA-LSTM (Deep Auto-Encoder LSTM) model are adopted for preprocessing of log data and anomaly-based detection to get better performance measures of detection. An enhanced LSTM (long-short-term memory) model, optimizing for the suitable parameter using a genetic algorithm (GA), is utilized to recognize better the anomaly from the log data that is filtered, adopting a Deep Auto-Encoder (DA). The Deep Neural network models are utilized to change over unstructured log information to training ready features, which are reasonable for log classification in detecting anomalies. These models are assessed, utilizing two benchmark datasets, the Openstack logs, and CIDDS-001 intrusion detection OpenStack server dataset. The outcomes acquired show that the DA-LSTM model performs better than other notable ML techniques. We further investigated the performance metrics of the ML and DL models through the well-known indicator measurements, specifically, the F-measure, Accuracy, Recall, and Precision. The exploratory conclusion shows that the Isolation Forest, and Support vector machine classifiers perform roughly 81%and 79%accuracy with respect to the performance metrics measurement on the CIDDS-001 OpenStack server dataset while the proposed DA-LSTM classifier performs around 99.1%of improved accuracy than the familiar ML algorithms. Further, the DA-LSTM outcomes on the OpenStack log data-sets show better anomaly detection compared with other notable machine learning models. Show more

Keywords: Anomaly detection, classification, deep learning, hyperparameter optimization, long short term memory model, artificial neural networks, openstack cloud

DOI: 10.3233/JIFS-201707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6257-6271, 2022

Authors: Zhou, Ya | Gao, Jinding

Article Type: Research Article

Abstract: In order to solve some optimization problems with multi-local optimal solutions, a plague infectious disease optimization (PIDO) algorithm is proposed by the dynamic model of plague infectious disease with pulse vaccination and time delay. In this algorithm, it is assumed that there are several villagers living in a village, each villager is characterized by some characteristics. The plague virus is prevalent in the village, and the villagers contract the infectious disease through effective contact with sick rats. The plague virus attacks is the few characteristics of the human body. Under the action of the plague virus, the growth status of …each villager will be randomly transformed among 4 states of susceptibility, exposure, morbidity and recovery, thus a random search is achieved for the global optimal solution. The physical strength degree of villagers is described by the human health index (HHI). The higher the villager’s HHI index, the stronger the physique and the higher the surviving likelihood. 9 operators (S_S, S_E, E_E, E_I, E_R, I_I, I_R, R_R, R_S) are designed in the PIDO algorithm, and each operator only deals with the 1/1000∼1/100 of the total number of variables each time. The case study results show that PIDO algorithm has the characteristics of fast search speed and global convergence, and it is suitable for solving global optimization problems with higher dimensions. Show more

Keywords: Swarm intelligence optimization, global optimization, plague transmission dynamic model, random search

DOI: 10.3233/JIFS-211092

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6273-6291, 2022

Authors: Karthika, A. | Subramanian, R. | Karthik, S.

Article Type: Research Article

Abstract: Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. …In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM. Show more

Keywords: Focal cortical dysplasia, T1- MPRAGE and T2- FLAIR, S2 feature extraction, lesion segmentation, recurrent neural network

DOI: 10.3233/JIFS-212463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6293-6306, 2022