Multi-Criteria Data Analysis with Interval-Valued Neutrosophic Sets to Decrease Plastic Pollution rivers

C. B. Aurelia; I. Q. Janneth; P. P. Alex

doi:https://doi.org/10.54216/IJNS.190136

Journal of Artificial Intelligence and Metaheuristics | 2022

Automatic Speech Recognition for Qur’an Verses using Traditional Technique

Deep learning is the one of approaches of machine learning that uses algorithms for building a model based on complex unstructured data. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, a traditional method to build a robust Qur’an versus recognition is proposed. The MFCC is used to extract features. These features are adapted using minimum phone error (MPE) as a discriminative model. The acoustic model was built using the deep neural network (DNN) model. We present an n-gram language model (LM). The dataset of Qur’an verses is used for training and evaluating the proposed model, consisting of 10 hours of .wav recitations performed by 60 reciters. The Experimental results showed that the proposed DNN model achieved a significantly low character error rate (CER) of 4.09% and a word error rate (WER) of 8.46%.

groups

Hamzah A. Alsayadi mail -

Mohammed Hadwan mail

link https://doi.org/10.54216/JAIM.010202

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2021

Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection

Explainable artificial intelligence received great research attention in the past few years during the widespread of Black-Box techniques in sensitive fields such as medical care, self-driving cars, etc. Artificial intelligence needs explainable methods to discover model biases. Explainable artificial intelligence will lead to obtaining fairness and Transparency in the model. Making artificial intelligence models explainable and interpretable is challenging when implementing black-box models. Because of the inherent limitations of collecting data in its raw form, data fusion has become a popular method for dealing with such data and acquiring more trustworthy, helpful, and precise insights. Compared to other, more traditional-based data fusion methods, machine learning's capacity to automatically learn from experience with nonexplicit programming significantly improves fusion's computational and predictive power. This paper comprehensively studies the most explainable artificial intelligent methods based on anomaly detection. We proposed the required criteria of the transparency model to measure the data fusion analytics techniques. Also, define the different used evaluation metrics in explainable artificial intelligence. We provide some applications for explainable artificial intelligence. We provide a case study of anomaly detection with the fusion of machine learning. Finally, we discuss the key challenges and future directions in explainable artificial intelligence.

groups

Ahmed Abdelmonem mail -

Nehal N. Mostafa mail

link https://doi.org/10.54216/FPA.030104

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2021

Deep Neural Network-based Fusion and Natural Language Processing in Additive Manufacturing for Customer Satisfaction

Modern Machine learning fusion approaches tend to extract features depending on two techniques (hand-crafted feature and representation learning). Hand-crafted features can waste time and are not sufficient for downstream tasks. Unlike representation learning, we automatically learn features with minimum time and effort and are suitable for downstream tasks. In our paper, we provide work on graph neural network methods with details on classical graph embedding approaches and the different methods in neural graph networks such as graph filtering, graph pooling, and the learning parameter for graph following each technique with a general framework or mathematical proof for customer satisfaction. To satisfy customer's feel, this research employs NLP techniques. We describe the adversarial attacks and defenses on graph representation approaches. Also, advanced application of neural graph networks is reviewed, such as combinational optimization, learning program representation, physical system modeling, and natural language processing. Finally, the challenges in geometric neural networks and future research work have been introduced.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/FPA.030105

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2021

Fusion of Machine learning for Detection of Rumor and False Information in Social Network

In recent years, spreading social media platforms and mobile devices led to more social data, advertisements, political opinions, and celebrity news proliferating fake news. Fake news can cause harm to networks, communications, and users and cause trust issues toward government, healthcare, or social media platforms. This inspired many researchers to implement models to detect falsified information content. But there are still many issues that need to be discussed and explored. In our paper, we introduce categories of fake news detection methods and compare these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and security and privacy of social media. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in social networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks to aid the readers and researchers in explaining the motive and role of the different machine learning fusion paradigms to offer them a comprehensive realization of unexplored issues related to false information and other scenarios of social networks.

groups

Nehal Mostafa mail -

IBRAHIM EL-HENAWY mail -

Ahmed Sleem mail

link https://doi.org/10.54216/FPA.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2022

Multi-Criteria Decision-Making Approach based on Neutrosophic Sets for Evaluating Sustainable Supplier Selection in the Industrial 4.0

Sustainability in supply chain management can be achieved by integrating its applications with Industry 4.0 platforms.  Considering the Sustainability and Industry 4.0 criteria for supplier selection, this research creates a new integrated model to improve the performance of the applicatios.  The choice of suppliers is evaluated using a two-stage neutrosophic sets and the EDAS method.  The first step of this research is to define all of the terms associated with Industry 4.0 and Sustainability.  The neutrosophic EDAS determines the relative relevance of each criterion.  The neutrosophic VIKOR method is used to rank the suppliers.  The suppliers' performance in meeting the sustainability and Industry 4.0 standards is then nominated in a two-stage neutrosophic sets.  A case study of a textile firm is offered to illustrate the usefulness of our integrated approach.  The effectiveness of the suggested integrated method is then evaluated via a series of sensitivity assessments.  Of the things we learned was that it's best to build a decision-making framework that uses Industry 4.0 and sustainability criteria to assess suppliers individually rather than in a relative fashion in a hazy setting.

groups

Mahmoud Ismail mail -

Mahmoud Ibrahiem mail

link https://doi.org/10.54216/AJBOR.070204

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2020

The Interplay Between Missing Data and Out-of-Order Measurements using Data Fusion in Wireless Sensor Networks

Multi-data transmission is the most important processing of target detection with a reduction in delay in the transmission of the data. This may occur in certain technological circumstances, and it happens significantly often in wireless sensor networks—processing such data to keep track of and make predictions about targets of interest might result in errors due to the inherent nature of the data. The Kalman filter and other algorithms with equivalent functionality are most useful for their principal application, estimating the states of dynamic systems. This difficulty of modeling and filtering such delayed states and missing data is dealt with synergistically throughout this proposed work. This is done to ensure that the best possible results are obtained. Filtering methods similar to the optimal Kalman filter are most utilized in fusing measurement data at different levels. This relatively creative technique includes filtering delayed states while also using observations that have been randomly excluded, then putting those screened delayed states and words to use in a process that involves fusing data. One of these applications is the fusion of images. To successful the task of performance evaluation for the integrated plan, the use of numerical simulations is essential. The state delay, as well as the data that is absent at random, are both included in four distinct alternative algorithms. These algorithms are then investigated, and the results are given in this paper. Referring to the gain fusion, the H-infinity a posteriori filter, the H-infinity risk sensitive filter, and the H-infinity risk sensitive filter. To accommodate a scenario that involves MATLAB and the integration of sensor data, global filtering approaches are being updated and evaluated with the use of numerical simulations that are being carried out. In addition, we provide a nonlinear observer based on the gain of the continuous time data fusion filter. Using the Lyapunov energy function, we can conclude on asymptotic convergence in the system.  These observers are presented after the previous step. Therefore, the filtering algorithms and the observers described in the current proposed work make a definite step towards improvement for controlling state delays and randomly missing data synergistically for wireless sensor networks.

groups

Piyush K. Shukla mail -

Ozen Ozer mail

link https://doi.org/10.54216/FPA.010202

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2020

Multimodal Image Fusion in Biometric Authentication

During this study, a unique multimodal biometric system was constructed. This system incorporated a variety of unimodal biometric inputs, including fingerprints, palmprints, knuckle prints, and retina images. The multimodal system generated the fused template via feature-level fusion, which combined several different biometric characteristics. The Gabor filter extracted the features from the various biometric aspects. The fusion of the extracted information from the fingerprint, knuckle print, palmprint, and retina into a single template, which was then saved in the database for authentication, resulted in a reduction in both the spatial and temporal complexity of the process. A novel technique for safeguarding fingerprint privacy has been developed to contribute to the study. This system integrates the unique fingerprints of the thumb, index finger, and middle finger into a single new template. It was suggested that the Fixed-Size Template (FEFST) technique may be used might develop a novel strategy for the extraction of fingerprint features. From each of the fingerprints, the minute locations of the ridge end and ridge bifurcations as well as their orientations relative to the reference points were retrieved. The primary template was derived from the fingerprint that included the greatest number of ridge ends. For the purpose of generating the combined minutiae template, the templates of the other two fingerprints were incorporated into this template. The merged minutiae template that was developed was then saved in a database so that registration could take place. During the authentication process, the system received the three query fingerprints, and those fingerprints were compared to the previously saved template.

groups

Uma Maheshwari mail -

Kalpanaka Silingam mail

link https://doi.org/10.54216/FPA.010203

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Next-Gen Urban Management: Automated Crowd Density Recognition using Rough Neutrosophic Sets for Smart Cities

Neutrosophic set (NS) and Neutrosophic logic (NL) play a major part in approximation theory. They are generalizations of intuitionistic fuzzy sets and logic correspondingly. Rough NS (RNS) combines the concepts of RS and NL to deal with vagueness, uncertainty, and imprecision in information. By integrating truth, indeterminacy, and false degrees, RNS provides a more solid basis for analyzing and classifying complicated data. Particularly, this makes it powerful in applications where incompleteness and ambiguity of data are ubiquitous. Smart cities are a current trend to contain information and communication technologies (ICTs) in the progression of great urban cities. It would be beneficial in defining the city's movement by monitoring the regular flow of traffic jams and visitors. One important characteristic of smart cities is Crowd management, which assists in safety and enjoyable experiences for the residents and visitors. Since the crowd density (CD) classification method encounters tasks including inter-scene, non-uniform density, and intra-scene deviations, occlusion and convolutional neural networks (CNNs) approaches were beneficial. This work focuses on the design of Automated Crowd Density Recognition using the Rough Neutrosophic Set for Smart Cities (ACDR-RNSSC) method in urban management. The presented ACDR-RNSSC method focuses on identifying various types of crowd densities in smart cities. Firstly, the ACDR-RNSSC method utilizes the ResNet50 method for feature extraction. Second, the classification is done using RNS. RNS is utilized for its ability to manage the vagueness and uncertainty in crowd density statistics. Lastly, the parameter is fine-tuned using the Fruit Fly Optimization Algorithm (FOA). This ensures that the model attains high robustness and accuracy in forecasting crowd density. The empirical analysis of the ACDR-RNSSC method is examined under benchmark crowd dataset and the outcomes are tested using various metrics. This study states the improvement of the ACDR-RNSSC method over existing techniques.

groups

Eaman Alharbi mail

link https://doi.org/10.54216/IJNS.250210

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2020

Pixel Level Image Fusion in Moving objection Detection and Tracking with Machine Learning

It is not feasible for a single image sensor to convey all of the information essential to comprehend a circumstance thoroughly. The output of many image sensors combined in one place would supply more accurate or comprehensive information on the topic at hand. In recent years, multi-sensor fusion has emerged in the academic world as an emerging topic that has the potential to produce beneficial results. This is because it can aggregate the data from several different sensors. One of the primary objectives is to devise various methods for combining kinematic and visual data to track a moving object. These methods should allow us to achieve this aim. This article looks into the intricacies of various techniques to evaluate the current condition of a target and explores the outcomes of those approaches. These sorts of methods include, for instance, the Kalman filter and its expanded version, the extended Kalman filter. The study of the proposed work is to demonstrate the specifics of the development of an interacting multiple-model Kalman filter to monitor the performance of the moving target in response to a wide variety of tuning parameters. The proposed technique includes the Principal Component Analysis and spatial frequency to integrate the hazy images that were all shot with the same sensor modalities. This action was taken to achieve the aimed-for outcome. The effectiveness of the fusion is evaluated based on the results of several distinct metrics.

groups

Piyush K. Pareek mail

link https://doi.org/10.54216/FPA.020105

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2022

Multi-Criteria Data Analysis with Interval-Valued Neutrosophic Sets to Decrease Plastic Pollution rivers

Whether for human consumption, agricultural production, or industrial processes, groundwater, and rivers, in particular, are crucial. The river is a major water source; therefore, we must do all we can to keep it clean. Inconsistencies in data, algorithms and expert judgments have contributed to the growing recognition of ambiguity analysis' significance. However, it is not common practice to include vulnerability assessment in MCA based Interval-Valued Neutrosophic Sets (IVNSs). To better understand what causes MCA uncertainty, this research examines the factors that contribute to it. Probabilistic techniques, indicator-based methods, and neutrosophic logic are examined as examples of broad approaches to analysis methods in MCA. The practicality, financial and ecological consequences, unexpected social and environmental implications, possible scale of change, and confirmation of the impact of plastic reduction strategies were investigated using an MCA technique.

groups

C. B. Aurelia Maria mail -

I. Q. Janneth Ximena mail -

P. P. Alex Javier mail

link https://doi.org/10.54216/IJNS.190136

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new