Forward feature selection: empirical analysis

Firuz; Said; Aswani; Annapurna

doi:https://doi.org/10.54216/JISIoT.110105

International Journal of Neutrosophic Science | 2023

Possibility neutrosophic bipolar fuzzy soft sets and their applications

In this work, the possibility neutrosophic bipolar soft sets interact with the possibility bipolar fuzzy soft sets, as well as complementation, union, intersection, AND, and OR. This paper extends the concept of bipolar neutrosophic soft sets to the possibility neutrosophic bipolar soft sets. Our main goal was to demonstrate De Morgan’s law, associate law and distributive law, which are all the laws related to the possibility neutrosophic bipolar soft sets. Also, we present an algorithm that uses a soft set model to solve the decision-making problem primarily in order to simplify the process.

groups

A. Priya mail -

P. Maragatha Meenakshi mail -

Aiyared Iampan mail -

N. Rajesh mail -

Suganthi Mariyappan mail

link https://doi.org/10.54216/IJNS.230116

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Assessment of Hazard in Firefighting Job Using Triangular Neutrosophic Sets and Hybrid Multi-criteria Decision Making

Traditional approaches to recognizing hazards and evaluating their risks have several shortcomings, including data confusion and unpredictability, an inability to accurately reflect human thought processes, a failure to give weight to factors, the use of established data and tables, and the influence of the evaluator on the final risk evaluation outcomes. Thus, refining current techniques and creating new ways with more precision and sensitivity is essential. We proposed a framework for risk assessment of firefighting. Firefighting has various criteria, so the concept of multi-criteria decision-making (MCDM) deals with these criteria, such as life safety, resource allocation, incident duration, weather conditions, access, etc. We collected ten risk criteria and 25 alternatives. The proposed framework has two main stages. First, we apply the average method to ten risk criteria to show the weights and the importance of the criteria. Then, in the second stage, we used the grey rational analysis (GRA) method to assess the firefighting risks. The GRA method is an MCDM methodology used to rank the alternatives. The GRA method is integrated with the triangular neutrosophic sets (TNSs) to deal with vague and uncertain information. Then, the principal results show that life safety is the highest weight, and the incident duration is the lowest. The outcome of the GRA method shows that risk 25 is the highest and risk 17 is the lowest. We applied the sensitivity analysis to show the stability of the results. We offer the model is adequate, and the results are stable. 

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Luz M. Aguirre Paz mail -

Maria Pico Pico mail

link https://doi.org/10.54216/IJNS.230117

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Single Valued Neutrosophic Sets Approach for Assessment Wind Power Plant

Full exploitation of offshore wind resources still needs to be completed despite their significant potential to reduce the impacts of climate change via the production of renewable power. Planning strategies that include wind resources, safety, economic, social, and government impacts are essential for advancing offshore wind generation projects. This study aims to evaluate the criteria for wind power plants and select the best turbine. This process has various conflict criteria, so the multi-criteria decision-making (MCDM) methodology deals with multiple criteria. The ARAS method is an MCDM method used to rank the alternatives. The ARAS method uses the single-valued neutrosophic set to deal with uncertain information. We gathered eleven criteria and fifteen alternatives. The results show the turbine resource is the best and the economic criterion is the worst. The sensitivity analysis is conducted to ensure the proposed model's results and show the strength of the proposed method. The results show the proposed model is suitable for selecting the best wind power plant.

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Dionisio Ponce Ruiz mail -

Giovanny Pineda Silva mail -

Maha Ibrahim mail

link https://doi.org/10.54216/IJNS.230118

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Choice Optimal Fuel Alternative in Thermal Power Station Using Neutrosophic Set and MCDM Methodology

In a power plant, the fuel choice directly impacts the efficiency, cost, and ecological impact of generating electricity. For power plants to produce electricity effectively and affordably to fulfill the needs of consumers in homes, companies, and communities, they need a fuel supply that is constant, dependable, and inexpensive. In this study, we used the concept of multi-criteria decision-making (MCDM) to deal with the various criteria of fuel alternatives. We used the EDAS method as an MCDM methodology to rank the fuel alternatives and select the best one. The EDAS method is employed with the interval-valued neutrosophic sets (IVNSs) to deal with the uncertainty information in the evaluation process. We compute the weights of the criteria of thermodynamic parameters. We used ten thermodynamic parameters such as temperature, mass, energy, etc. Then, the principal results show that temperature is the best criterion, and the work interaction is the worst criterion in all criteria. The EDAS method ranked twenty alternatives. The results show that alternative 20 are the best and alternative 14 is the worst of all alternatives. We employed the sensitivity analysis to show the rank of alternatives under ten cases. The results show the 20 alternative is the best in all cases. The results are stable.    

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Edmundo Jalon Arias mail -

Luis Freire Lescano mail -

Giovanny Pineda Silva mail -

Maha Ibrahim mail

link https://doi.org/10.54216/IJNS.230119

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Intelligent Classification of JPEG files by Support Vector Machines with Content-based Feature Extraction

Nowadays, multimedia files play a basic role in supporting evidence analysis for making decisions about a crime through looking at files as a digital guide or evidence. Multimedia files such as JPG images are a common format because many documents and memorial images on laptops are valuable. In addition, many JPG images on Laptops are valuable and have fewer structure contents, making recovery possible when their file system is missing. However, intelligent systems for fully recovering corrupted JPG images into their original form is a challenging research issue. In this research, a support vector machine (SVM) as intelligent classifier algorithm is proposed to classify JPG or non-JEG image clusters as part of multimedia files. The SVM classifies the data clusters on three content-based feature extraction (entropy, byte frequency distribution, and rate of change approach to derive cluster features) methods to optimize the identification of JPG image content. The SVM classifier is applied using a radial basis and polynomial kernel functions in MATLAB software. The experimental results show that the accuracy of classification of the SVM classifier with the polynomial function is 96.21%, and the SVM classifier with the radial basis function is 57.58%.

groups

Rabei Raad Ali mail -

Najwan Zuhair Waisi mail -

Yahya Younis Saeed mail -

Mohammed S. Noori mail -

Eko Hari Rachmawanto mail

link https://doi.org/10.54216/JISIoT.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Early Energy Consumption Prediction as a Key Element in Smart City Sustainability

In the era of smart cities, the pursuit of sustainability stands as a paramount goal, with energy management playing a central role. This paper is dedicated to the exploration of early energy consumption prediction as a linchpin in the realization of sustainable smart cities. Employing advanced long short-term memory (LSTM) networks, we introduce a potent predictive model tailored to anticipate energy consumption patterns within urban environments. Notably, our model achieves remarkable performance metrics, with a root mean square error of 547.71 and a strikingly low mean absolute percentage error (MAPE) of 1.22. Through meticulous comparisons against baseline models, our LSTM-based approach emerges as a beacon of accuracy, reliability, and sustainability. Beyond predictive analytics, our research offers actionable insights for urban planners and policymakers, fostering the creation of greener, more sustainable, and ecologically responsible smart cities that harmonize technological innovation with environmental stewardship. As smart cities continue to evolve, our work lays the foundation for a future where sustainability is not merely a goal but a reality.

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Fausto Vizcaino Naranjo mail -

Silvio Machuca Vivar mail -

Edmundo Jalon Arias mail -

Reem Atassi mail

link https://doi.org/10.54216/JISIoT.110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Security and Privacy Protection for Online Electronic Documents Based on Novel Encryption Techniques

Corporate strategies have employed techniques that enter the domain of shadow and espionage in this rapidly developing, technologically competitive business environment. Supporting a security strategy is a way to counter these possible dangers. To preserve corporate success in the marketplace, network security needs to be crucial to the protection of electronic documents. Encryption technology has become more important in recent years for protecting online digital documents. This research was motivated by the fact that document verification has become quite time-consuming and difficult due to a variety of challenging and laborious processes. Existing technologies often malfunction when a single kind of encryption, such as AES, Data Encryption Standard (DES), or Rivest, Shamir, Adleman (RSA), is utilized at the request of the customer. Therefore, this study proposes hybrid cryptography, which integrates two novel algorithms into existing encryption protocols. A digital signature is generated for the data when a user uploads a data. The data are encrypted in parallel using the suggested Secured Hash Function-256 (SHA-256) method with improved DES and RSA (SHA-256+Enhanced DES+RSA). The proposed encryption method was shown to be more accurate than previous studies in experimental evaluations of  data encryption.

groups

Amer Ibrahim mail -

Ravi Sekhar mail -

Jamal Fadhil Tawfeq mail -

Sinan Q. Salih mail -

Pritesh Shah mail -

Ahmed Dheyaa Radhi mail

link https://doi.org/10.54216/JISIoT.110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis

Advanced technologies such as the Internet of Things provide an integrated platform for weather focusing, including rainfall and flood prediction. Large rainfall data frequently contain noise, which can be difficult to analyze using a standard time series model due to violated assumptions. Singular spectrum analysis (SSA) is a model-free time series analysis method that is widely used. This study aims to predict the rainfall trends in the Special Region of Yogyakarta, Indonesia, using the Recurrent SSA (SSA-R) and Vector SSA (SSA-V). The SSA-R forecasts using the recurrent continuation directly with the linear recurrent formula, while the SSA-V is a modified recurrent method. This study used 50 years of monthly rainfall data (1970-2019) from 25 stations in the special region of Yogyakarta, Indonesia. The SSA steps for forecasting rainfall data include decomposition (embedding and singular value decomposition), reconstruction (grouping and diagonal averaging), and evaluating the SSA model using w-correlation (if w-correlation is close to zero, returning to the decomposition stage; otherwise, continue the process), forecasting, evaluating the forecast results using root mean square error (RMSE), mean absolute error, r, and mean forecast error, and finally selecting the best model (either the SSA-R or SSA-V model). The results showed that the SSA-R performed better than SSA-V due to the smallest RMSE in the dry, rainy, and inter-monsoon seasons. The SSA-R model’s forecast results revealed faint, constant patterns for the dry, and rainy seasons and an increasing pattern for the inter-monsoon season. The novelty of this study is to compare the performance of the SSA-R and SSA-V models in the large rainfall data in the special region of Yogyakarta, Indonesia.

groups

Kismiantini mail -

Shazlyn Milleana Shaharudin mail -

Ezra Putranda Setiawan mail -

Dhoriva Urwatul Wutsqa mail -

Muhamad Afdal Ahmad Basri mail -

Hairulnizam Mahdin mail -

Salama A. Mostafa mail

link https://doi.org/10.54216/JISIoT.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Wetland Mapping by Fusion of Deep learning and Ensemble Model for Enhancing Prediction Outcomes

Constraints perceived in different socioeconomic situations reinforce land use patterns and land cover (LULC) at different levels. However, the statistical information regarding the LULC variations encounters enormous significance for the execution and modelling of appropriate environmental variations and resource management with the available remote sensed data from diverse satellite images and advanced computing technologies; information is generally retrieved from the image classification approaches. However, a broader quantitative analysis of various classification approaches is crucial to choosing an effectual classifier model to acquire appropriate land use regions. We concentrate on the Karavetti region and its related fields in this study. We use a Non-Linear Recurrent Convolutional Neural Network (NLR-CNN) to analyze the data statistically. Well-known techniques such as Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), among others are used to evaluate the model performance. High-resolution images and the data points supplied are also used to assess the accuracy of the categorization and prediction. A confusion matrix is generated where the land cover regions show superior classification accuracy with the fusion model. Also, the NDVI facts and additional metrics like loss, error rate and kappa coefficients are analyzed. Therefore, the outcomes show that the anticipated is considered more robust with better performance to enhance the classification accuracy with the specific land cover regions.

groups

Thylashri S. mail -

Rajalakshmi N. R. mail

link https://doi.org/10.54216/FPA.140115

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Forward feature selection: empirical analysis

Feature selection is an important preprocessing step in many data science and machine learning applications. Although there exist several sophisticated feature selection algorithms, their benefits are sometimes overshadowed by their complexity and slow execution. Therefore, in many cases, a more simple algorithm may be better suited. In this paper, we demonstrate that a rudimentary forward selection algorithm can achieve optimal performance with a low time complexity. Our study is based on an extensive empirical evaluation of the forward feature selection algorithm in the context of linear regression. Concretely, we compare the forward selection algorithm against the gold standard exhaustive search algorithm based on several datasets. The results show that the forward selection algorithm achieves high performance with relatively fast execution. Given the simplicity, accuracy, and speed of the forward feature selection algorithm, we recommend it as a primary feature selection method for most regression applications. Our results are particularly pertinent in the case of big data and real-time analysis.

groups

Firuz Kamalov mail -

Said Elnaffar mail -

Aswani Cherukuri mail -

Annapurna Jonnalagadda mail

link https://doi.org/10.54216/JISIoT.110105

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new