Journal of Intelligent & Fuzzy Systems - Volume 45, issue 3

Authors: Sasirekha, N. | Karuppaiah, Jayakumar | Shekhar, Himanshu | Naga Saranya, N.

Article Type: Research Article

Abstract: Cancer is a devastating disease that has far-reaching effects on our culture and economy, in addition to the human lives it takes. Regarding budgetary responsibility, investing just in cancer treatment is not an option. Early diagnosis is a crucial part of the remedy that sometimes gets overlooked. Malignancy is often diagnosed and evaluated using Histopathology Images (HI), which are widely accepted as the gold standard in the field. Yet, even for experienced pathologists, analysing such images is challenging, which raises concerns of inter- and intra-observer variability. The analysis also requires a substantial investment of time and energy. One way that …such an examination may be sped up is by making use of computer-assisted diagnostics devices. The purpose of this research is to create a comprehensive cancer detection system using images of breast and prostate histopathology stained with haematoxylin and eosin (H&E). Proposed here is work on improving colour normalisation methods, constructing an integrated model for nuclei segmentation and multiple objects overlap resolution, introducing and evaluating multi-level features for extracting relevant histopathological image and interpretable information, and developing classification algorithms for tasks such as cancer diagnosis, tumor identification, and tumor class labelling. Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network which obtained statistical kappa value for breast cancer histopathology images shows a high degree of consistency in the classification task, with a range of 0.610.80 for benign and low grades and a range of 0.811.0 for medium and high rates. The Support Vector Machine (SVM), on the other hand, shows an almost perfect degree of consistency (0.811.0) across the several breast cancer picture classifications (benign, low, medium, and high). Show more

Keywords: Breast cancer, Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network, computer-assisted diagnostic systems, histopathology images

DOI: 10.3233/JIFS-231480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4651-4667, 2023

Authors: Sahayaraj, L. Remegius Praveen | Muthurajkumar, S.

Article Type: Research Article

Abstract: Preserving the integrity of log data and using the same for forensic analysis is one of the prime concerns of cloud-oriented applications. Since log data collates sensitive information, providing confidentiality and privacy is of at most importance. For data auditors, maintaining the integrity of the log data is a prime concern. Existing models focus on providing models and frameworks that relies on any third-party entity or the cloud service provider (CSP) to handle the logs, which lacks in securing the integrity due to the presence of the external entities. Sole dependence on CSP is a major flaw together with a …drawback, since the CSP itself is prone to data theft alliance. In this paper, we instantiate a mechanism which maintains the integrity of the log without compromising the performance efficiency of the system. The influence of machine learning classification techniques is leveraged in order to efficiently classify the log data before it is processed. Progressively the log data integrity is maintained through the proposed Propagated Chain of Log Blocks (PCLB), the Hybrid Vector Committed BST (HVCBST) and lightweight Multikey Hybrid Storage (MKHS) structures. The results of the implemented systems have proven to be efficient and tamper proof compared to the existing systems and can be easily rendered in any private or public cloud deployments. Show more

Keywords: Data integrity, cloud, security, log, block chain, encryption, decryption

DOI: 10.3233/JIFS-224585

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4669-4687, 2023

Authors: Hou, Jia-Ning | Zhang, Min | Wang, Jie-Sheng | Wang, Yu-Cai | Song, Hao-Ming

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-230081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4689-4714, 2023

Authors: Zhang, Yiwen | Zhang, Li | Dong, Yunchun | Chu, Jun | Wang, Xing | Ying, Zuobin

Article Type: Research Article

Abstract: Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and …predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model. Show more

Keywords: Knowledge graph, rating prediction, collaborative filtering

DOI: 10.3233/JIFS-230795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4715-4724, 2023

Authors: Zhang, Fang | Wang, Hongjuan | Wang, Lukun | Wang, Yue

Article Type: Research Article

Abstract: Human body pose transfer is to transform the character image from the source image pose to the target pose. In recent years, the research has achieved great success in transforming the human body pose from the source image to the target image, but it is still insufficient in the detailed texture of the generated image. To solve the above problems, a new two-stage TPIT network model is proposed to process the detailed texture of the pose-generated image. The first stage is the source image self-learning module, which extracts the source image features by learning the source image itself and further …improves the appearance details of pose-generated image. The other stage is to change the pose of the figure gradually from the source image pose to the target pose. Then, by learning the feature correlation between source and target images through cross-modal attention, texture transmission between images is promoted to generate finer-grained details of the generated image. A large number of experiments show that the model has superior performance on the Market-1501 and DeepFashion datasets, especially in the quantitative and qualitative evaluation of Market-1501, which is superior to other advanced methods. Show more

Keywords: Posture transfer, self-attention mechanism, dual-tasking mechanism, character image generation, generating adversarial networks

DOI: 10.3233/JIFS-231289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4725-4735, 2023

Authors: Ponniah, Krishna Kumar | Retnaswamy, Bharathi

Article Type: Research Article

Abstract: The internet of things (IoT) has significantly influenced day-to-day life in large industrial systems. The Internet of Things (IoT) offers a platform for information systems to integrate effectively with network servers. In contrast, cyber threats are becoming critical, especially for IoT servers. A strong strategy must be in place to protect the network system from multiple attacks. In order to detect malicious behaviors that deteriorate network performance, an intrusion detection system (IDS) is crucial. An IDS use a detection method to monitor network activity to alert IoT users regularly. This paper proposes a novel IDS for IoT using log-sigmoid kernel …principal component analysis (LSK-PCA) and activation updated deep feed-forward neural network (AU-DFFNN) based dimensionality reduction (DR) and classification technique. Initially, the input data is taken from the NSLKDD dataset and undergoes pre-processing. Afterwards, attribute extraction is carried out, followed by Fisher’s Yates Adapted Golden Eagle Optimizer (FY-GEO) based feature selection. Then, DR of the feature selected data is done using the LSK-PCA model. Finally, the reduced dataset is given as an input to the classifier for classifying the data as attacked and normal data. As a final point, experimental analysis is performed using performance metrics like precision (PR), recall (RC), f-score (FS), accuracy (AC), false alarm rate (FAR) and computational time (CT). The results proved that the proposed work detects intrusion effectively compared to state-of-art techniques. Show more

Keywords: Intrusion Detection System (IDS), Internet of Things (IoT), Golden Eagle Optimizer (GEO), Feed Forward Neural Network (FFNN), Attribute extraction, Dimensionality reduction, Principal Component Analysis (PCA)

DOI: 10.3233/JIFS-223437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4737-4751, 2023

Authors: Ashok Kumar, M. | Saravanan, K.

Article Type: Research Article

Abstract: In multicasting packets of data from a node will be sent to a group of receiver nodes at the same time. Multicasting lowers transmission costs. Energy conservation is critical to a sensor network’s long-term viability. Sensor networks have limited and non-replenishable energy supplies, maximizing network lifetime is crucial in sensor nodes. As a result, clustering has become one of the popular methods for extending the lifetime of an entire system by integrating information at the cluster head. Cluster head (CH) selection is the important serving node in each cluster in the Wireless sensor networks (WSN). This paper introduces a High …Power Node (HPN) multicasting approach which embeds a cluster of sink node data in packet headers to allow receiver for utilizing a approach for transferring multicast packet data via the shortest paths. The proposed Energy efficient multicasting cluster based routing (EEMCR) protocol utilized high power nodes, which shall play a critical role in minimal energy usage. The implementation findings demonstrate that, when compared with the previous methodologies, the suggested algorithm has enhanced in terms of packet delivery ratio (PDR), End to end delivery rate, efficiency and achieves low energy consumption. The proposed EEMCR obtain 95% efficiency. The results are then compared to other existing algorithms to determine the superiority of the proposed methodology. Show more

Keywords: Routing, wireless sensor networks, multicasting, cluster head selection, clustering

DOI: 10.3233/JIFS-223536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4753-4766, 2023

Authors: Nathezhtha, T. | Vaidehi, V. | Sangeetha, D.

Article Type: Research Article

Abstract: In recent days, malicious users try to captivate the consumers using their fraudulent marketing URL post in social networking sites. Such malicious URL posted by fake users in Social Networking Services (SNS) is hard to identify. Therefore, there occurs a need to detect such fraudulent URLs in SNS. In order to detect such URLS, this paper proposes a SNS Fraudulent Detection (SFD) scheme. The proposed SFD scheme includes a Deterministic Finite Automata Tokenization (DFA-T) and Web Crawler (WC) based Neuro Fuzzy System (WC-NFS). DFA-T extracts the URL features and calculates a Penalty Score (PS) based on the malicious words in …the extracted URL. The DFA extracted URL features with PS are fed into WC-NFS. Subsequently, the WC fetches the numeric WC-Index (WCI) value from the URLs which are added to the WC-NFS. The existing URL data set is used to identify the malicious web links and suitable machine learning techniques are used to identify the malicious URLs. From the experimental results, it is found that the proposed SFD provides 92.6 % accuracy in classifying the benign from malicious URLs when compared with the existing methods. Show more

Keywords: Consumer electronics, fraudulent, web crawler, social networking service, malicious users

DOI: 10.3233/JIFS-223569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4767-4775, 2023

Authors: Bai, Zhiqiang | Yang, Zhiyong | Jiang, Yusheng | Gao, Hongji | Sun, Zhengyang | Sun, Wei

Article Type: Research Article

Abstract: The earth pressure balance (EPB) shield tunneling efficiency is greatly affected by the choice of soil transport mode. In this study, the influence of two soil transport modes, such as the continuous belt conveyor and rail train, on the efficiency of shield excavation was analyzed using the Markov chain model. A method was proposed to define the ideal and non-ideal excavation states and quantitatively evaluate the excavation efficiency of the two soil transportation modes of the EPB shield. Based on this model framework, a profitable Markov chain model was established to predict the expected profits of the two soil transportation …modes. The Beijing Metro New Airport Line first-phase project was used as a case study to verify the model established. The results show that under the same conditions, the continuous belt conveyor soil transport mode can have a higher excavation efficiency and expected profit. This advantage gradually increases over time. Show more

Keywords: Markov chain, soil transport, excavation efficiency, expect profit, shield construction

DOI: 10.3233/JIFS-223833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4777-4790, 2023

Authors: Jegajothi, B. | Kathir, I. | Shukla, Neeraj Kumar | Prakash, R.B.R.

Article Type: Research Article

Abstract: Because of environmental issues and energy crises, significant attention has been received in the domain of renewable and clean energy systems. Solar energy is the most effective source of renewable energy technologies. Recently, photovoltaic (PV) system have become common in grid-linked applications and plays a vital part in power production. MPPT algorithms enable PV systems to capture the maximum available power from the solar panels, regardless of variations in solar irradiance, temperature, and other environmental factors. By continuously tracking the MPP, MPPT techniques ensure that the PV system operates at its highest efficiency, resulting in increased energy harvesting and improved …overall performance. Meanwhile, the frequent modifications in irradiance and temperature pose a major challenging issue which can be resolved by the use of artificial intelligence MPPT methodologies like artificial neural networks (ANN), fuzzy logic (FL), and metaheuristics systems. In this aspect, this work presents a new quasi-oppositional artificial algae optimization (QOAAO) with an adaptive neuro-fuzzy inference system (ANFIS) technique, named QOAAO-ANFIS for maximum efficiency MPPT technique for minimizing the present ripple and power oscillations over the MPP. The presented QOAAO-ANFIS model mainly depends upon the integration of the ANFIS and QOHOA techniques. In addition, the presented QOAAO-ANFIS model involves optimal MF selection of the ANFIS model to estimate the irradiation level and compute PV voltage equivalent to maximal power point. The QOAAO model can be utilized for enhancing the optimization process of membership function variables under varying conditions and awareness of global optima. The simulation result analysis of the QOAAO-ANFIS model takes place in terms of different evaluation measures. Extensive comparative results reported the better performance of the QOAAO-ANFIS model with maximum tracking efficiency of 99.89% and a minimum convergence time of 13.51 ms. Show more

Keywords: Membership function, photovoltaic systems, maximum power point tracking, artificial intelligence, fuzzy logic controller

DOI: 10.3233/JIFS-223889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4791-4805, 2023