Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Abdus Subhahan, D. | Vinoth Kumar, C.N.S.

Article Type: Research Article

Abstract: The worldwide deforestation rate worsens year after year, ultimately resulting in a variety of severe implications for both mankind and the environment. In order to track the success of forest preservation activities, it is crucial to establish a reliable forest monitoring system. Changes in forest status are extremely difficult to manually annotate due to the tiny size and subtlety of the borders involved, particularly in regions abutting residential areas. Previous forest monitoring systems failed because they relied on low-resolution satellite images and drone-based data, both of which have inherent limitations. Most government organizations still use manual annotation, which is a …slow, laborious, and costly way to keep tabs on data. The purpose of this research is to find a solution to these problems by building a poly-highway forest convolution network using deep learning to automatically detect forest borders so that changes over time may be monitored. Here initially the data was curated using the dynamic decomposed kalman filter. Then the data can be augmented. Afterward the augmented image features can be fused using the multimodal discriminant centroid feature clustering. Then the selected area can be segmented using the iterative initial seeded algorithm (IISA). Finally, the level and the driver of deforestation can be classified using the poly-highway forest convolution network (PHFCN). The whole experimentation was carried out in a dataset of 6048 Landsat-8 satellite sub-images under MATLAB environment. From the result obtained the suggested methodology express satisfied performance than other existing mechanisms. Show more

Keywords: Deforestation, dynamic decomposed kalman filter, multimodal discriminant centroid feature clustering, iterative initial seeded algorithm, poly-highway forest convolution network

DOI: 10.3233/JIFS-233534

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Agrawal, Monika | Moparthi, Nageswara Rao

Article Type: Research Article

Abstract: Sentiment analysis (SA)at the sentence, aspect, and document levels determines the sentiment of particular aspect phrases in a given sentence. Due to their capacity to extract sentiment information from text in aspect-level sentiment classification, neural networks (NNs) have achieved significant success. Generally speaking, sufficiently sizable training corpora are necessary for NNs to be effective. The performance of NN-based systems is reduced by the small size of the aspect-level corpora currently available. In this research, we suggest a gated bilateral recurrent neural network (G-Bi-RNN) as a foundation for multi-source data fusion, their system offers sentiment information that several sources. We develop …a uniform architecture specifically to include information from sentimental lexicons, including aspect- and sentence-level corpora. To further provide aspect-specific phrase representations for SA, we use G-Bi-RNN, a deep bilateral Transformer-based pre-trained language model. We assess our methods using SemEval 2014 datasets for laptops and restaurants. According to experimental findings, our method consistently outperforms cutting-edge techniques on all datasets. We use a number of well-known aspect-level SA datasets to assess the efficacy of our model. Experiments show that when compared to baseline models, the suggested model can produce state-of-the-art results. Show more

Keywords: Sentiment analysis (SA), gated bilateral recurrent neural network (G-Bi-RNN), language model

DOI: 10.3233/JIFS-234076

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Pughazendi, N. | Valarmathi, K. | Rajaraman, P.V. | Balaji, S.

Article Type: Research Article

Abstract: Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the …entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework. Show more

Keywords: Internet of Things (IoT), big data, cloud, clustering, health care solution, slot allocation, Random Forest Deep Neural Network (RF-DNN), categorization

DOI: 10.3233/JIFS-233505

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Subburaj, S. | Murugavalli, S. | Muthusenthil, B.

Article Type: Research Article

Abstract: SLR, which assists hearing-impaired people to communicate with other persons by sign language, is considered as a promising method. However, as the features of some of the static SL could be the same as the feature in a single frame of dynamic Isolated Sign Language (ISL), the generation of accurate text corresponding to the SL is necessary during the SLR. Therefore, Edge-directed Interpolation-based Recurrent Neural Network (EI-RNN)-centered text generation with varied features of the static and dynamic Isolated SL is proposed in this article. Primarily, ISL videos are converted to frames and pre-processed with key frame extraction and illumination control. …After that, the foreground is separated with the Symmetric Normalised Laplacian-centered Otsu Thresholding (SLOT) technique for finding accurate key points in the human pose. The human pose’s key points are extracted with the Media Pipeline Holistic (MPH) pipeline approach and to improve the features of the face and hand sign, the resultant frame is fused with the depth image. After that, to differentiate the static and dynamic actions, the action change in the fused frames is determined with a correlation matrix. After that, to engender the output text for the respective SL, features are extracted individually as of the static and dynamic frames. It is obtained from the analysis that when analogized to the prevailing models, the proposed EI-RNN’s translation accuracy is elevated by 2.05% in INCLUDE 50 Indian SL based Dataset and Top 1 Accuracy 2.44% and Top 10 accuracy, 1.71% improved in WLASL 100 American SL. Show more

Keywords: Isolated Sign Language (ISL), Sign Language Recognition (SLR), Edge directed Interpolation based Recurrent Neural Network (EIRNN), text generation, word level sign language, Media Pipeline Holistic (MPH)

DOI: 10.3233/JIFS-233610

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Prasath, N. | Arun, A. | Saravanan, B. | Kamaraj, Kanagaraj

Article Type: Research Article

Abstract: Intelligent Fuzzy Edge Computing (IFEC) has emerged as an innovative technology to enable real-time decision-making in Internet of Things (IoT)-based Digital Twin environments. Digital Twins provide virtual models of physical systems, facilitating predictive maintenance and optimization. However, implementing real-time decision-making in these environments is challenging due to massive data volumes and need for quick response times. IFEC addresses this by offering a flexible, scalable and efficient platform for real-time decision-making. This paper presents an overview of key aspects of IFEC including fuzzy logic, edge computing and Digital Twins. The use of fuzzy logic in IFEC provides an adaptive framework for …handling uncertainties in data. Edge computing enables localized processing, reducing latency. The integration of Digital Twins allows system monitoring, analysis and optimization. Potential applications of IFEC are highlighted in domains such as manufacturing, healthcare, energy management and transportation. Recent advancements in IFEC are also discussed, covering new fuzzy inference systems, edge computing architectures, Digital Twin modeling techniques and security mechanisms. Overall, IFEC shows great promise in enabling real-time decision-making in complex IoT-based Digital Twin environments across various industries. Further research on IFEC will facilitate the ongoing digital transformation of industrial systems. Show more

Keywords: Intelligent fuzzy edge computing, real-time decision making, IoT-based digital twins, predictive maintenance, fuzzy logic, edge computing

DOI: 10.3233/JIFS-233495

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Vishnukumar, Ravula | Ramaiah, Mangayarkarasi

Article Type: Research Article

Abstract: The Internet’s evolution resulted in a massive amount of data. As a result, the internet has become more sophisticated and vulnerable to massive attacks. The attack detection system is a key feature for system security in modern networks. The IDS might be signature-based or detect anomalous behavior. Researchers recently created several detection algorithms for identifying network intrusions in vehicular network security, but they failed to detect intrusions effectively. For this reason, the optimal Deep Learning approach, namely Political Fractional Dingo Optimizer (PFDOX)-based Deep belief network is introduced for attack detection in network security for vehicles. The Internet of Vehicle simulation …is done initially, and then the input data is passed into the pre-processing phase, which removes noise present in the data. Then, the feature extraction module receives the pre-processed data. The Deep Maxout Network is trained using the Fractional Dingo optimizer (FDOX)is utilized to detect normal and abnormal behavior. Fractional calculus and Dingo optimizer (DOX) are combined to create the proposed FDOX. Finally, intruder/attack types are classified using the Deep Belief Network, which is tuned using the PFDOX. The PFDOX is created by the assimilation of the DOX, Fractional Calculus, and Political Optimizer (PO). The experimental result shows that the designed PFDOX_DBN for attack type classification offers a better result based on f-measure, precision, and recall with the values of 0.924, 0.916, and 0.932, for the CIC-IDS2017 dataset. Show more

Keywords: Deep maxout network, intrusion detection, deep belief network, dingo optimizer, fractional calculus, political optimizer

DOI: 10.3233/JIFS-233581

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2023

Authors: Nandipati, Bhagya Lakshmi | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: Lung cancer incidence and mortality continue to rise rapidly around the world. According to the American Cancer Society, the five-year survivability for individuals in the metastasis phases is significantly lower, highlighting the importance of early lung cancer diagnosis for effective therapy and improved quality of life. To achieve this, it is crucial to combine PET’s sensitivity for recognizing abnormal regions with CT’s anatomical localization for evaluating PET-CT images in computer-assisted detection implementations. Current PET-CT image evaluation methods either run each modality independently or aggregate the data from both, but they often overlook the fact that different visual features encode different …types of data from different modalities. For instance, high atypical PET uptake within the lungs is more crucial for identifying tumors compared to physical PET uptake in the heart. To address the challenges of fine-grained issues during feature extraction and fusion, we propose an interpretable deep learning-based solution for lung cancer diagnosis using CT and PET images. This involves building an Optimal Adversarial Network for merging and an Optimal Attention-based Generative Adversarial Network with Classifier (Opt_att-GANC) to augment the classification of the existence and nonexistence of lung cancer based on extracted features. The performance of the Opt_att-GANC is compared with existing methodologies like global-feature encoding U-Net (GEU-Net), 3D Dense-Net, and 3D Convolutional Neural Network Technique (3D-CNN). Results show that the proposed Opt_att-GANC achieves an F1-score of 67.08%, 93.74% accuracy, 92% precision, 92.1% recall, and 93.74% recall. The prospective study aims to enhance the precision degree with reduced duration by incorporating an ensemble neural network paradigm for feature extraction. Show more

Keywords: Lung cancer, fuzzy fusion, feature extraction, classification, neural networks, Adversarial network, PET

DOI: 10.3233/JIFS-233491

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Arulmurugan, A. | Jose Moses, G. | Gandhi, Ongole | Sheshikala, M. | Arthie, A.

Article Type: Research Article

Abstract: In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be …employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity. Show more

Keywords: Quasi-oppositional, feature selection, Flamingo Search Algorithm, Simulated Annealing, convergence rate

DOI: 10.3233/JIFS-233557

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Harikumar, Yedhu | Muthumeenakshi, M.

Article Type: Research Article

Abstract: The Indian stock market is a dynamic, complicated system that is impacted by many different variables, making it difficult to anticipate its future. The utilization of deep learning and optimization techniques to forecast stock market movements has gained popularity in recent years. To foresee the Indian stock market, an innovative approach is presented in this study that combines the Grey Wolf Optimization algorithm with a hybrid Convolutional Neural Network (CNN) and Bi-Directional Long-Short Term Memory (Bi-LSTM) framework. The stock market information is first pre-processed utilizing a CNN to extract pertinent features. The Bi-LSTM system, that is intended to capture the …long-term dependencies and temporal correlations of the stock market statistics, is then fed the CNN’s outcome. The model parameters are then optimized utilizing the Grey Wolf Optimization (GWO) technique, which also increases forecasting accuracy. The findings demonstrate that, in terms of forecasting accuracy, the suggested method outperforms a number of contemporary methods, including conventional time series models, neural networks, and evolutionary algorithms. Thus, the suggested methodology provides an effective way to forecast the Indian stock market by combining deep learning and optimization approaches. Show more

Keywords: Indian stock market, grey wolf optimization, deep learning approach, bi-directional long-short term memory, convolutional neural network

DOI: 10.3233/JIFS-233716

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023

Authors: Vallabhaneni, Nagalakshmi | Prabhavathy, Panneer

Article Type: Research Article

Abstract: Numerous people are interested in learning yoga due to the increased tension levels in the modern lifestyle, and there are a variety of techniques or resources available. Yoga is practiced in yoga centers, by personal instructors, and through books, the Internet, recorded videos, etc. As the aforementioned resources may not always be available, a large number of people will opt for self-study in fast-paced lifestyles. Self-learning makes it impossible to recognize an incorrect posture. Incorrect poses will have a negative effect on the patient’s health, causing severe agony and long-term chronic issues. Computer vision (CV)-related techniques derive pose features and …conduct pose analysis using non-invasive CV methods. The application of machine learning (ML) and artificial intelligence (AI) techniques to an inter-disciplinary field like yoga becomes quite difficult. Due to its potent feature learning ability, deep learning (DL) has recently achieved an impressive level of performance in classifying yoga poses. In this paper, an artificial algae optimizer with hybrid deep learning-based yoga pose estimation (AAOHDL-YPE) model is presented. The presented AAOHDL-YPE model analyzes yoga video clips to estimate pose. Utilizing Part Confidence Map and Part Affinity Field with bipartite equivalent and parsing, OpenPose can be employed to determine the joint location. The deep belief network (DBN) model is then used for Yoga recognition. Finally, the AAO algorithm is utilized to enhance the EfficientNet model’s recognition performance. The results of a comprehensive experimentation analysis reveal that the AAOHDL-YPE technique produces superior results in comparison to existing methods. Show more

Keywords: Yoga posture, activity recognition, deep learning, metaheuristics, computer vision

DOI: 10.3233/JIFS-233583

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023