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

Authors: Sendhil, R. | Arulmurugan, A. | Jose Moses, G. | Kaviarasan, R. | Ramadoss, P.

Article Type: Research Article

Abstract: Occult peritoneal metastasis often emerges in sick persons having matured gastric cancer (GC) and is inexpertly detected with presently feasible instruments. Due to the existence of peritoneal metastasis that prevents the probability of healing crucial operation, there relies upon a discontented requirement for an initial diagnosis to accurately recognize sick persons having occult peritoneal metastasis. The proffered paradigm of this chapter identifies the initial phases of occult peritoneal metastasis in GC. The initial phase accompanies metabolomics for inspecting biomarkers. If the sick person undergoes the initial signs of occult peritoneal metastasis in GC, early detection is conducted. Yet, the physical …prognosis of this cancer cannot diagnose it, and so, automated detection of the images by dissecting the preoperational Computed Tomography (CT) images by conditional random fields accompanying Pro-DAE (Post-processing Denoising Autoencoders) and the labeling in the images is rid by denoising strainers; later, the ensued images and the segmented images experience the Graph Convolutional Networks (GCN), and the outcome feature graph information experience the enhanced categorizer (Greywold and Cuckoo Search Naïve Bayes categorizer) procedure that is employed for initial diagnosis of cancer. Diagnosis of cancer at the initial phase certainly lessens the matured phases of cancer. Hence, this medical information is gathered and treated for diagnosing the sickness. Show more

Keywords: Gastric Cancer, MIoT, Greywold and Cuckoo Search Naïve Bayes categorizer, Cuckoo-Grey Wolf search Correlative Naïve Bayes categorizer

DOI: 10.3233/JIFS-233510

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

Authors: Priya, S. Baghavathi | Rani, P. Sheela | Chokkalingam, S.P. | Prathik, A. | Mohan, M. | Anitha, G. | Thangavel, M. | Suthir, S.

Article Type: Research Article

Abstract: Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes …an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy. Show more

Keywords: Zero Knowledge Proof, RSA accumulator, educational certificates, cross-domain authentication, blockchain

DOI: 10.3233/JIFS-235140

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023

Authors: Lakshmi Narayanan, K. | Naresh, R.

Article Type: Research Article

Abstract: Vehicular Ad-Hoc Network (VANET) Technology is advancing due to the convergence of VANET and cloud computing technologies, Vehicular Ad-Hoc Network (VANET) entities can benefit from the cloud service provider’s favourable storage and computing capabilities. Cloud computing, the processing and storage capabilities provided by various cloud service providers, would be available to all VANET enterprises. Digital Twin helps in creating a digital view of the Vehicle. It focuses on the physical behaviour of the Vehicle as well as the software it alerts when it finds issues with the performance. The representation of the Vehicle is created using intelligent sensors, which are …in OBU of VANET that help collect info from the product. The author introduces the Cloud-based three-layer key management for VANET in this study. Because VANET connections can abruptly change, critical negotiation verification must be completed quickly and with minimal bandwidth. When the Vehicles are in movement, we confront the difficulty in timely methods, network stability, and routing concerns like reliability and scalability. We must additionally address issues such as fair network access, inappropriate behaviour identification, cancellation, the authentication process, confidentiality, and vehicle trustworthiness verification. The proposed All-Wheel Control (AWC) method in this study may improve the safety and efficiency of VANETs. This technology would also benefit future intelligent transportation systems. The Rivest–Shamir–Adleman (RSA) algorithm and Chinese Remainder Theorem algorithms generate keys at the group, subgroup, and node levels. The proposed method produces better results than the previous methods. Show more

Keywords: Cloud computing, VANET, RSA, CRT, AWC

DOI: 10.3233/JIFS-233527

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

Authors: Kadry, Heba | Samak, Ahmed H. | Ghorashi, Sara | Alhammad, Sarah M. | Abukwaik, Abdulwahab | Taloba, Ahmed I. | Zanaty, Elnomery A.

Article Type: Research Article

Abstract: Coronavirus is a new pathogen that causes both the upper and lower respiratory systems. The global COVID-19 pandemic’s size, rate of transmission, and the number of deaths is all steadily rising. COVID-19 instances could be detected and analyzed using Computed Tomography scanning. For the identification of lung infection, chest CT imaging has the advantages of speedy detection, relatively inexpensive, and high sensitivity. Due to the obvious minimal information available and the complicated image features, COVID-19 identification is a difficult process. To address this problem, modified-Deformed Entropy (QDE) algorithm for CT image scanning is suggested. To enhance the number of training …samples for effective testing and training, the suggested method utilizes QDE to generate CT images. The retrieved features are used to classify the results. Rapid innovations in quantum mechanics had prompted researchers to use Quantum Machine Learning (QML) to test strategies for improvement. Furthermore, the categorization of corona diagnosed, and non-diagnosed pictures is accomplished through Quanvolutional Neural Network (QNN). To determine the suggested techniques, the results are related with other methods. For processing the COVID-19 imagery, the study relates QNN with other existing methods. On comparing with other models, the suggested technique produced improved outcomes. Also, with created COVID-19 CT images, the suggested technique outperforms previous state-of-the-art image synthesis techniques, indicating possibilities for different machine learning techniques such as cognitive segmentation and classification. As a result of the improved model training/testing, the image classification results are more accurate. Show more

Keywords: Coronavirus, quantum machine learning, quanvolutional neural network, Q-deformed entropy

DOI: 10.3233/JIFS-233633

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

Authors: Pradeep, M. | Sivaji, U. | Nithya, B. | Kadiravan, G. | Preethi, D. | Painam, Ranjith Kumar

Article Type: Research Article

Abstract: The mapping function must identify the reference model and detect coordinate arrangement by observing a repository with deep learning. Progression model with coordinate arrangement composition should have various positional displacements from one location to another. A prerogative classification model is an evolution of factor accomplishment in a repository method. Coordinate arrangement with calculation method must formulate a model locality twirl in classification method of a reference in dominance factor of perpetuity position observation by procession of reference localities. In a procession model observation by location, tendency method should be rotated from locality position into another coordinate method, with a PDD …factor measuring DPA of cadent RFT with an origin of 92.6, a cadent DS intermediate factor of 95.2, culmination factor of cadent RFT of 94.1. The docile exploratory arrangement of heuristic parameters is used in existing system to perceive phenomena such as sprout, enrollment discernment, demeanour, gravest perforation measure, Model of a heretic in apprehension method by premonition incongruity. Annotation should identify classification process using a proposed model to obtain massive measure of imputation function, In PDD measure of DPA in Cadent DS, with inception of 96.1, intercession of Cadent RFT in 92.6, with crowning of Cadent RFT in 96.4, 93.2 Show more

Keywords: MRCAI, Goin Twirl, maginot, idiosyncrasy outline, coffer atavism, flocculent utter eminence kedge

DOI: 10.3233/JIFS-234739

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

Authors: Ahamed, Ayoobkhan Mohamed Uvaze | Joel Devadass Daniel, D.J. | Seenivasan, D. | Rukumani Khandhan, C. | Radhakrishnan, S. | Daya Sagar, K.V. | Bhardwaj, Vivek | Nishant, Neerav

Article Type: Research Article

Abstract: Time-sensitive programs that are linked to smart services, such as smart healthcare as well as smart cities, are supported in large part by the fog computing domain. Due to the increased speed limitation of the cloud, Cloud Computing (CC) is a competent platform for fog in data processing, but it is unable to meet the demands of time-sensitive programs. The procedure of resource provisioning, as well as allocation in either a fog-cloud structure, takes into account dynamic changes in user requirements, and resources with limited access in fog devices are more difficult to manage. Due to the continual changes in …user requirement factors, the deadline represents the biggest obstacle in the fog computing structure. Hence the objective is to minimize the total cost involved in scheduling by maximizing resource utilization. For dynamic scheduling in the fog-cloud computing model, the efficiency of hybridization of the Grey Wolf Optimizer (GWO) and Lion Algorithm (LA) is developed in this study. In terms of energy costs, processing costs, and communication costs, the created GWOMLA-based Deep Belief Network (DBN) performed better and outruns the other traditional models. Show more

Keywords: Fog-cloud computing environment, deep learning, deep belief network (DBN), lion algorithm (LA), grey wolf optimizer (GWO).

DOI: 10.3233/JIFS-234030

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Kalaipriya, O. | Dhandapani, S.

Article Type: Research Article

Abstract: Lung cancer is one of the leading causes of mortality from cancer. Lung cancer is a kind of malignant lung tumor characterized by uncontrolled cell proliferation in lung tissues. Even though CT scans are the most often used imaging technology in medicine, clinicians find it challenging to interpret and diagnose cancer from CT scan pictures. As a result, computer-aided diagnostics can assist clinicians in precisely identifying malignant cells. Many computer-aided approaches were explored and applied, including image processing and machine learning. A comparison of the various classification methodologies will assist in enhancing the accuracy of lung cancer detection systems that …employ robust segmentation and classification algorithms presented in this research. This research proposed to enhance existing segmentation and classification-basedmethodsof human lung cancer detection with optimization in techniques. The workflow includes initial preprocessing of medical images, for segmentation a novel hybrid methodology is developed by combining enhanced k-means clustering and random forest and classification with an Artificial neural network enhanced with PSO parameter and feature optimization. Show more

Keywords: Machine learning, K-means, ANN, random forest, PSO, image processing technique

DOI: 10.3233/JIFS-233845

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

Authors: Wang, Jing | Gao, Tingting | Du, Hongxu | Tu, Chuang

Article Type: Research Article

Abstract: To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate …the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development. Show more

Keywords: Community group-buying, the route problem of end-distribution, improved genetic algorithm, carbon emission cost

DOI: 10.3233/JIFS-234773

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Ayub, Mohammed | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: Energy is a critical resource for daily activities and lifestyles with direct impacts on the economy, health and environment. Therefore, monitoring its efficient use is essential to reduce energy waste and lessen related concerns such as global warming and climate change. One of the prominent and evolving solutions is Non-Intrusive Load Monitoring (NILM) smart meters, which enables consumers to track their per-appliance energy consumption more effectively. Some recent approaches have proposed deep learning as a powerful tool for energy disaggregation. However, it is difficult to employ these models in resource-constrained end devices for effective energy monitoring. In this paper, we …explore and evaluate a lightweight improved model for multi-target non-intrusive load monitoring based on MobileNet architectures. With extensive experiments using the ENERTALK dataset, the results show that MobileNetV3-large is the most appealing for energy disaggregation as it requires about 55% less storage for trained model and about 6% less training time than MobileNetV2 with almost the same performance. On average, version 3 large has a 17.63% reduction in SAE and requires 54.21% and 8.93% less space and less training time than version 2, respectively. Moreover, the average performance is boosted using an ensemble multi-target MobileNet model across all houses, leading to significant reduction of MAE, SAE, and RMSE errors of about 6%, 48%, and 4%, respectively. In comparison to other work, the proposed MMNet-NILM shows superior performance for the majority of appliances in terms of all considered evaluation metrics. Show more

Keywords: Multi-target MobileNet, ENERTALK, Lightweight NILM, energy disaggregation, ensemble MobileNet

DOI: 10.3233/JIFS-219426

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024

Authors: Sindge, Renuka Sambhaji | Dutta, Maitreyee | Saini, Jagriti

Article Type: Research Article

Abstract: Video Super Resolution (VSR) applications extensively utilize deep learning-based methods. Several VSR methods primarily focus on improving the fine-patterns within reconstructed video frames. It frequently overlooks the crucial aspect of keeping conformation details, particularly sharpness. Therefore, reconstructed video frames often fail to meet expectations. In this paper, we propose a Conformation Detail-Preserving Network (CDPN) named as SuperVidConform. It focuses on restoring local region features and maintaining the sharper details of video frames. The primary focus of this work is to generate the high-resolution (HR) frame from its corresponding low-resolution (LR). It consists of two parts: (i) The proposed model decomposes …confirmation details from the ground-truth HR frames to provide additional information for the super-resolution process, and (ii) These video frames pass to the temporal modelling SR network to learn local region features by residual learning that connects the network intra-frame redundancies within video sequences. The proposed approach is designed and validated using VID4, SPMC, and UDM10 datasets. The experimental results show the proposed model presents an improvement of 0.43 dB (VID4), 0.78 dB (SPMC), and 0.84 dB (UDM10) in terms of PSNR. Further, the CDPN model set new standards for the performance of self-generated surveillance datasets. Show more

Keywords: Super-resolution, image super-resolution, video super-resolution, recurrent network, residual learning

DOI: 10.3233/JIFS-219393

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024