Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach

Marcelo Y.; Oswaldo T.; Diego P.; S. K.

doi:https://doi.org/10.54216/JISIoT.110106

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

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American Journal of Business and Operations Research |

Deep Learning Empowered Phishing URL Detection an Exhaustive Approach

Cybercriminals continually exploit users' vulnerabilities deceptive URLs through phishing attacks are a significant threat to both individuals and organizations. Cybercriminals regularly use phishing to trick users giving them permission to use corporate networks and digital files. Faster Recurrent Convolutional Neural Network (FRCNN) has been proposed to automatically identify phishing websites. However, there are certain drawbacks to its approach: (1) When the URL is converted into a characteristic matrix, there is a storage restriction, making it impossible to gather the embedding vector of new phrases to the actual data of sensitive characters; (2) it is also impossible to acquire the URL's long-distance dependent characteristic. Based on existing system, hybrid model Bidirectional Long Short Term Memory (Bi-LSTM) and FRCNN proposed to identify the phishing attack. The proposed system enables to obtain URL long-distance dependent characteristics by combining two current URL division approaches. Phishing websites can be quickly and accurately identified based on their URLs using the Extreme Gradient Boosting (XGBOOST) and Naïve Bayes Method. According to experimental findings, this approach can produce high F1 values, recall rates and accuracy levels.

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SubashiniKsuba.pooja@gmail.com mail

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Prospects for Applied Mathematics and Data Analysis |

Generalized fractional Burgers model front travelling wave solution, double soliton, and its interplay

The fractional Hirota bilinear technique is employed in this publication to calculate the analytical solution for the hyperbolic generalized space-time fractional Burgers model. For the intended fractional differential model under consideration, we develop a double soliton wave. To verify the results, these computations are carried out using symbolic computing tools like Maple. Richer structures can be constructed thanks to the fractional orders' random selection. More applications in the applied sciences may result from soliton alterations based on fractional order adjustments

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khalidAdamkhalid_adam7@yahoo.com mail

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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%.

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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

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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

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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.

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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

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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.

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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

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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.

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Thylashri S. mail -

Rajalakshmi N. R. mail

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

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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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.

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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

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Journal of Intelligent Systems and Internet of Things | 2024

Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach

This study delves into optimizing sustainable inventory management practices through the integration of advanced data analytics methodologies. In response to the complex dynamics of modern supply chains, where inventory control significantly impacts sustainability goals, this research aims to address the intricate interplay between decentralized decision-making, government policies, and strategic choices within supply chain networks. Employing models such as Game Theory and Gated Recurrent Unit (GRU), alongside statistical analyses, our investigation explores the transformative potential of informed decision-making frameworks. Through a comprehensive evaluation of inventory data, including statistical analyses, visual representations, and model evaluations, we illuminate the nuanced relationships and dependencies prevalent within inventory control strategies. Our findings underscore the significance of data-driven decision-making in optimizing inventory practices, mitigating risks, and fostering sustainability within supply chains. The insights gleaned from this study advocate for the continued application of advanced data analytics to pave the way for resilient, environmentally conscious, and economically viable supply chain practices.

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Marcelo Y. Villacis mail -

Oswaldo T. Merlo mail -

Diego P. Rivero mail -

S. K. Towfek mail

link https://doi.org/10.54216/JISIoT.110106

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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