Pollution Reduction using Intelligent Warning Messages in VANET

Esraa Al-Ezaly, Ahmed Abo-Elfetoh and Sara

doi:https://doi.org/10.54216/JISIoT.030202

American Journal of Business and Operations Research | 2021

A Blockchain-Based Voting System for E-Elections in Totalitarian States

The purpose of the present research was to introduce a blockchain-based voting system so that any state, including totalitarian states, can show interest in using it. In this method, a hybrid voting system with two centralized and distributed systems was used. Its centralized system is one of the most common voter identification and polling models, and its distributed system, which is designed with Ethereum public blockchain, is voting for voters. Totalitarian states are not interested in announcing the results online. Also, the lack of trust in E-voting systems by both states and voters has led to E-voting in important political elections in most states as support for manual or paper voting. Based on the results of field research with this voting system, it was possible to create a 7 min break between the end of the voting process and the announcement of the results for political considerations. This break can be increased by agreement. The results of the votes cannot be manipulated in any way. Survey results should also be communicated to voters before the voting process. This voting system can improve the level of democracy and maximum participation. It is hoped that the spread of distributed technologies, especially the blockchain, will pave the way for the spread of justice and democracy around the world.

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Mohammad Reza Esfandyari mail -

Mohammad Hossin shafiabadi mail

link https://doi.org/10.54216/AJBOR.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Fusion: Practice and Applications | 2020

Diabetes prediction system using ml & dl techniques

Diabetes nowadays is a familiar and long-term disease. If a prediction is made early, better treatment can be provided. The preprocessing data approach is extremely useful in predicting the disease at an early stage. "Many tools are used in determining significant characteristics such as selection, Prediction, and association rule mining for diabetes. The principal component analysis method was used to select significant attributes. Our judgments denote a strong association of diabetes with body mass indicator (BMI) and glucose degree. The study implemented logistic regression, decision trees, and ANN techniques to process Pima Indian diabetes datasets and predict whether people at risk have diabetes. It was analyzed that random forest had the best accuracy of 80.52 %. Out of 500 negative records & 268 positive records, our model correctly analyzed 403 records & 216 records, respectively.

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Nandini Gupta mail -

Shubhangi Malik mail -

Hardik Chawla mail -

Surinder Kaur mail

link https://doi.org/10.54216/FPA.010201

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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American Journal of Business and Operations Research | 2020

Blockchain Technology in Supply Chain Management: A Review of Business Applications and Future Directions

Blockchain technology has the potential to revolutionize supply chain management (SCM) by increasing transparency, improving efficiency, and reducing costs. This paper reviews the business applications of blockchain technology in SCM and identifies future directions for its use. We explore the current applications of blockchain technology in SCM, including tracking and tracing of goods, verification of product authenticity, and automating supply chain processes. Then, we examine the benefits and challenges of implementing blockchain technology in SCM and discuss the potential impact on various stakeholders, including suppliers, manufacturers, distributors, retailers, and consumers. Following, we identify future directions for research and development in blockchain technology for SCM, including the integration of AI and ML, the use of smart contracts, and the development of new blockchain-based business models.

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Dina K. Hassan mail -

Ahmed K. Metawee mail -

Bassem Hassan mail

link https://doi.org/10.54216/AJBOR.010201

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Vol. Volume 1 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things |

Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

In the 21st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG. In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well.

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NavodneranjanThilakarathne mail

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Journal of Intelligent Systems and Internet of Things | 2021

A Dockers Storage Performance Evaluation: Impact of Backing File Systems

This paper reports on an in-depth examination of the impact of the backing filesystems to Docker performance in the context of Linux container-based virtualization. The experimental design was a 3x3x4 arrangement, i.e., we considered three different numbers of Docker containers, three filesystems (Ext4, XFS and Btrfs), and four application workloads related to Web server I/O activity, e-mail server I/O activity, file server I/O activity and random file access I/O activity, respectively. The experimental results indicate that Ext4 is the most optimal filesystem, among the considered filesystems, for the considered experimental settings. In addition, the XFS filesystem is not suitable for workloads that are dominated by synchronous random write components (e.g., characteristical for mail workload), while the Btrfs filesystem is not suitable for workloads dominated by random write and sequential write components (e.g., file server workload).  

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Amer Ramadan mail

link https://doi.org/10.54216/JISIoT.030101

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2021

A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring

A fall of an older adult often leads to severe injuries and is found to be a significant reason for the death due to post-traumatic complications. Many falls happen in the home atmosphere and prevail unrecognized. Thus, the need for reliable early fall detection is necessary for fast help. Lately, the emergence of wearables, smartphones, IoT, etc., made it possible to develop systems fall detection which aids in the remote monitoring of the elderly. The goal is to allow intelligent algorithms and smartphones to detect falls for elderly care and to monitor them regularly. This work presents the Artificial Intelligence of Things for Fall Detection (AIOTFD) system using a slime mould algorithm (SMA) to optimize the final data. The features extracted using SqueezeNet further CNN based SMA used for data optimization. The validation of the AIOTFD model performance is evaluated through the Multiple Cameras Fall Dataset (MCFD) and UR Fall Detection dataset (URFD). The empirical results accentuated the assuring realization of the model compared to other state-of the art methods.The obtained results shows our proposed AIOTFD attains accuracy of 99.82% and 99.79% and databases can be used for additional investigation and optimizations to increase the recognition rate to enhance the independent life of the elderly.

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Noushini Nikeetha mail -

Kirubasri G.V. mail -

Haritha Sasikumar mail -

Yazhini Tamilanban mail -

Jagruti Patil mail -

Gopinath mail

link https://doi.org/10.54216/JISIoT.030102

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2021

Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions

In the 21st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG.  In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well.

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Navod Neranjan Thilakarathne mail -

Rohan Samarasinghe mail -

Mohan Krishna Kagita mail -

Surekha Lanka mail -

Hussain Ahmad mail

link https://doi.org/10.54216/JISIoT.030103

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2021

A new way of Reaching consumers: the Role of Marketing Related Mobile Factors on the Consumers' Acceptance of Multichannel Mobile Marketing

This research investigates factors that may affect the level of acceptance of mobile marketing and determines if there is a relationship between these factors and the level of acceptance of mobile marketing. It separately investigates the most influential factors affecting the level of acceptance. This research was to investigate as well if there were differences in the readiness of undergraduate students regarding acceptance and factors leading to acceptance in terms of gender, age, education, and place. The research depends upon a sample of undergraduate students studying in universities. The researcher employed statistical techniques such as descriptive, correlation analysis, linear multiple regression, one-way Anova, and the post hoc test. The main findings from this research are that factors affecting acceptance were related to the level of acceptance of mobile marketing in the research field of reality. There is a significant difference between undergraduate students regarding factors affecting acceptance of mobile marketing; also there is a significant difference between undergraduate students regarding their readiness of acceptance in terms of some demographic characteristics.

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Alaa Elsayed Elsayaad mail

link https://doi.org/10.54216/AJBOR.030203

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2021

Intelligent System for Ranking Big Data in Search Engine

The spread of Internet sources has increased the volume of big data that is difficult to handle in traditional ways. So, most users need modern search systems to facilitate the search and retrieval of information in the presence of big data. However, the main challenge in the first and second conventional generations of search engines are linking different web data based on the syntax of keywords not on the semantic meaning and without a knowledge base. This manuscript proposes a framework based on modern technologies such as ETI processes, ontology graphs, and indexing RDF using wide column NoSQL technique. The main contribution of our work is introducing a mathematical model that is used to calculate the similarity score between a query and stored RDF documents based on semantic relations. Various operations were carried out to measure the proposed model's efficiency using data sources such as DBpedia, YAGO dataset. According to experimental results, the proposed model is achieving high precision compared to other related systems.

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M.M.El-Gayar mail -

M. EL-Hasnony mail

link https://doi.org/10.54216/JISIoT.030201

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2021

Pollution Reduction using Intelligent Warning Messages in VANET

Many conferences all over the world about environmental protection are situated. Air pollution resulted is an urgent issue for all people on the earth. Crowded cars in the intersections in traffic light intersections are one of the causes of air pollution. Also, rapid accelerations and deacceleration in the intersection cause air pollution. They also lead to packet transmission delay. This paper treats these issues using an intelligent warning message which reduces crowded cars, rapid accelerations, and deacceleration. Using vehicular ad hoc networks (VANETs), intelligent warning messages are used. Results show that our system outperforms previous studies such as traffic light control and pre-timed method in transmission delay, CO2 emission which causes air pollution.

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Esraa Al-Ezaly, Ahmed Abo-Elfetoh and Sara Elhishi mail

link https://doi.org/10.54216/JISIoT.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new