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

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