Algorithms for Computing Pythagoras Triples and 4-Tiples in Some Neutrosophic Commutative Rings

Hamiyet; Ahmed

doi:https://doi.org/10.54216/IJNS.200310

Prospects for Applied Mathematics and Data Analysis | 2023

A Study of the Movement of a Neutrosophic Material Point in the Neutrosophic Plane by Using a Neutrosophic AH-Isometry

This articles presents a new concept in mathematics according to a neutrosophic logic, which is the first of its kind in applied mathematics, which is the concept of the movement of a neutrosophic point in the neutrosophic plane and determining the path of this point, after determining Cartesian and polar neutrosophic coordinates of this point and also defining the neutrosophic local, velocity and acceleration vectors of these point. We have also determined the relationsships between the motion of a neutrosophic point and its equivalent in the classical plane, through an AH-isometry that connects the neutrosophic and classical plane. We conducted the previous study on three different examples, each example differs from the other in the form of a path and type of movement of the studied point.

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Mountajab Alhasan mail -

Malath F Alaswad mail

link https://doi.org/10.54216/PAMDA.020101

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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International Journal of Neutrosophic Science |

Application of Neutrosophic filters in Lattice implication algebra

Neutrosophic set theory is applied to lattice implication algebras and the concept of neutrosophic filters and neutrosophic lattice filters in lattice implication algebra are introduced. Several properties are investigated. Characterizations of a neutrosophic filter are discussed. Finally, we proved that every neutrosophic filter is a neutrosophic lattice filter but the converse is invalid.

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International Journal of Neutrosophic Science | 2023

Single-Valued Quadripartitioned Neutrosophic Minimal Structure Space

The main objective of this article is to procure the notion of single-valued quadripartitioned neutrosophic minimal structure space (SVQNMSS) and introduce the notion of single-valued quadripartitioned neutrosophic minimal open set and single-valued quadripartitioned neutrosophic minimal continuous function in it. Further, we study several fundamental properties of continuity in SVQNMSS, such as the composition of single-valued quadripartitioned neutrosophic minimal continuous functions and the product of single-valued quadripartitioned neutrosophic minimal functions in product SVQNMSS.

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Suman Das mail -

Rakhal Das mail -

Prasanna Poojary mail -

Vadiraja Bhatta G. R. mail

link https://doi.org/10.54216/IJNS.200302

Volume & Issue

Vol. Volume 20 / Iss. Issue 3

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Journal of Intelligent Systems and Internet of Things | 2019

Intelligent Asset Tracking System for Logistics Industry using IoT and Big Data

The logistics industry is a complex and dynamic ecosystem that requires efficient and reliable asset tracking systems (IATS) to optimize operations and reduce costs. To address these challenges, an IATS is proposed in this paper that leverages the power of IoT and big data technologies to collect real-time data on the location, condition, and status of assets such as trucks, containers, and shipments. The system is designed to provide end-to-end visibility and control of assets throughout the logistics value chain. It uses a combination of RFID, GPS, and other tracking technologies to collect data on asset location, temperature, humidity, vibration, and other relevant parameters. The data is then transmitted to a cloud-based platform for storage, processing, and analysis using big data analytics and machine learning algorithms. The platform enables logistics companies to monitor and manage their assets in real-time, optimize routes and schedules, and improve delivery times. It also provides machine learning tools for predictive modeling of asset price movement, enabling companies to identify potential price changes before they occur and minimize loss. The efficiency and effectiveness of our system were shown through simulation studies using data from real-world assets; as a result, it is an attractive option for the tracking and management of assets in real-world logistic businesses.

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Salah-ddine KRIT mail

link https://doi.org/10.54216/JISIoT.000104

Volume & Issue

Vol. Volume 0 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2019

IoT-Based Health Monitoring System with Real-Time Analytics

Thanks to advances in nanodevices and internet technology, it is now possible for devices from different manufacturers to be connected and communicate with one another. Among the fields that benefited from this technology integration were healthcare and general well-being. Healthcare had been established to lower healthcare expenses and offer enhanced and dependable services. Nevertheless, the primary difficulty in building such systems has continually been ensuring a high quality of service (QoS) in terms of quicker reaction and complicated analysis of data, given the sensitive and medical data. To solve these problems, this article suggests a heterogeneous Health Monitoring System built on mist, fog, and the cloud that can process and route data in both immediately and in form of a batch. In addition, the proposed system uses software-defined networking and load-stabilizing method to make sure that all available resources are being used effectively and efficiently.  Experimental simulations validated that our system could achieve excellent QoS, with acceptable delay and packet delivery rate, which is crucial for the creation of sustainable healthcare solutions.

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Salah-ddine KRIT mail

link https://doi.org/10.54216/JISIoT.000205

Volume & Issue

Vol. Volume 0 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2021

Smart Irrigation System with Predictive Analytics using Machine Learning and IoT

Water scarcity is a significant issue in agriculture, making efficient irrigation practices crucial for sustainable farming.  Integration of Internet of Things (IoT) and machine learning technologies are becoming of great importance to improve irrigation efficiency and reduce water usage. In this paper, we propose an intelligent irrigation system that take the advantage of IoT to improve the predictive analytics of groundwater levels. Our system used a deep learning to estimate the groundwater level using convolutional recurrent model that analyzed the sensory measurements necessary to predict groundwater levels. The model is trained on a large dataset of time series records and corresponding groundwater levels, allowing it to learn the complex patterns and relationships between time series features and groundwater levels. The experimental predictive analytics provided accurate irrigation recommendations, and the remote monitoring capabilities allowed farmers to adjust the irrigation schedule as needed.

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Ahmed Sleem mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/JISIoT.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2020

Intelligent Waste Management System for Recycling and Resource Optimization

This paper proposes a deep learning-based intelligent waste management system that can accurately classify waste types and optimize waste disposal processes. The proposed system utilizes a convolutional model to concisely identify the waste type from images captured by a camera system. Our system uses intelligent data augmentation to perform large datasets of waste item images and achieves a high classification accuracy rate. The waste types are classified into several categories, including glass, cardboard, metal, plastic, paper, and trash. Experimental results show that our system achieves high accuracy rates in waste classification and improves waste disposal efficiency compared to traditional waste management systems. Our system has the potential to significantly reduce the negative impact of waste on the environment and to promote sustainable waste management practices.

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Ahmed Sleem mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/JISIoT.010205

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2021

Intelligent Traffic Management System for Smart Cities

rapid urbanization and the growing population in smart cities pose significant challenges to the management of urban traffic. In recent years, there has been an increasing interest in developing intelligent traffic management systems that leverage advanced machineries, such as the Internet of Things (IoT), and machine learning (ML), to enhance the efficiency and effectiveness of traffic management in smart cities. This paper proposes an intelligent traffic management (ITM) system for smart cities that integrates various computing paradigms to provide real-time traffic information, optimize traffic flow, and improve road safety.  The suggested system utilizes an innovative system for the predicting the traffic flows with the goal of enhancing the current level of traffic management in smart cities. An enhanced convolutional autoencoder network is incorporated into the proposed system as a means of extracting the spatial representations contained in traffic flows. Additionally, by the utilization of a refined gated learning module, it possesses the capability of accurately recording temporal dynamics. Our system is evaluated using real-world traffic data, and the results demonstrate its effectiveness in improving traffic flow and reducing congestion in smart cities. Our system has the potential to significantly enhance the performance of traffic management systems in smart cities, decrease traffic crowding, and progress the safety of roads in smart cities.

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Mahmoud Ismail mail -

Shereen Zaki mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2021

Intelligent Energy Management System for Sustainable Smart Homes

Energy management in smart homes involves the use of technology to optimize energy consumption, reduce waste, and lower energy costs. Smart homes are equipped with various devices, sensors, and systems that are designed to monitor and control energy usage.  We proposed a novel Energy Management System (EMS) that integrates Machine Learning (ML) techniques and IoT paradigms to optimize energy consumption and reduce energy costs for sustainable smart homes. In addition to the AI-based EMS, we propose integrating fog computing, a decentralized computing infrastructure, to improve the speed, accuracy, privacy, and security of the EMS. The fog nodes can collect data from the various sensors and devices in the smart home and process the data in real time, reducing latency and allowing for quicker decision-making. By processing data at the edge of the network, fog computing also reduces the amount of data that needs to be sent to the cloud, improving privacy and security. Experimental proof-of-concept simulations demonstrated the efficiency and effectiveness of our system in improving sustainability in smart homes.

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Mahmoud Ismail mail -

Shereen Zaki mail -

Heba Rashad mail

link https://doi.org/10.54216/JISIoT.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023