Modeling Sports Event Tasks in Augmentative and Alternative Communication Using Deep Learning

Noora Hani; Eay; Najlaa Nsrulaah; Hussein Alaa; Raaid; Seifedine

doi:https://doi.org/10.54216/JISIoT.090207

International Journal of Advances in Applied Computational Intelligence | 2023

Industry 5.0: Theoretical Foundations for Enabling Technologies and Applications in Manufacturing Context

Industry 4.0, also known as the fourth industrial revolution, is a concept that refers to an increased degree of automation with the purpose of increasing operational productivity and efficiency in an industry by integrating the virtual and physical worlds. As a result of the inability of Industry 4.0 to answer and fulfill the rising demand for personalization, the phrase "Industry 5.0" was established to address personalized production and to empower individuals in the manufacturing process. The introduction of the phrase "Industry 5.0" has been met with various perspectives about how it should be defined and what aspects of coexistence between people and robots should be prioritized. This acts as the impetus for this work in identifying and analyzing the many topics and research trends of what Industry 5.0 is employing text mining tools and methodologies. In this article, a comprehensive discussion of the possible applications of Industry 5.0, including intelligent healthcare, cloud manufacturing, supply chain management, and production in the manufacturing industry, is presented. After that, we will talk about some of the enabling technologies for Industry 5.0, such as edge computing, digital twins, collaborative robots, the Internet of Everything (IoE), blockchain, and networks that are 6G and beyond. In conclusion, we discuss a number of research obstacles and unresolved questions that need to be further investigated in order to realize the potential of Industry 5.0. In recent years, it has come to the attention of the scientific community that the concept of Industry 5.0 as a doorway leading to the connectedness and co-existence of humans and machines has been garnering a growing amount of interest.

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Ahmed Abdelhafeez mail

link https://doi.org/10.54216/IJAACI.040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Digital Twin: An Investigation of the Characteristics, Visions, Challenges, and Opportunities

Emerging technology, known as digital twin (DT) is surrounded by numerous promises and potentials to influence the future of industries and society as a whole. A DT is a system of systems that goes much beyond conventional computer simulations and analyses. It is the process of replicating all of the components, processes, and dynamics of a physical system into their corresponding digital counterparts. Both the physical and digital systems coexist in the same space, sharing all of the inputs and activities via the use of real-time data transfers and the exchange of information. The DT provides a platform for testing and assessing complicated systems, which is not achievable with conventional simulations or modular assessments. This is one of the many benefits offered by the DT. However, the development of this technology faces many challenges, such as the complexities in effective communication and data accumulation, the lack of data available to train machine learning (ML) models, the lack of processing power to support high-fidelity twins, the high need for collaboration between different fields of study, and the absence of standardized development methodologies and validation measures. Due to the fact that DTs are still in the early phases of development, little documentation exists. In this light, the purpose of this survey article is to make attempt to address the significant facets involved in the actualization of the technology. The most important enabling technologies, constraints, and opportunities associated with DTs are discussed. The article presents an in-depth analysis of the technology, includes a list of design aims and objectives, analyses research and commercial advances, details the applications of the technology, and identifies obstacles and constraints associated with design across many sectors.

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Ahmed Abdel-Rahim EI-Douh mail -

Ayman H. Abdel-aziem mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

A Normalized Weighted Bonferroni Mean Aggregation Operator in Neutrosophic Vague Multi-Criteria Decision- Making

Decision-making problems involve uncertain and incomplete information, which can be well represented by the Neutrosophic set (NS). Various extensions of NS are available in the literature for solving such problems. However, the published extensions of NS have some restrictions such as single based membership degree. Neutrosophic vague set (NVS) is a newly developed theory to address the shortcomings of previous set theory. NVS is structured based on interval membership in the context of dependent membership functions. Beside uncertainty information, aggregation operators (AOs) are critical components in real-world decision-making issues. As a generalization to the conventional aggregation functions defined on the set of real numbers, numerous AOs have been presented in the literature. Each AO provides a distinct purpose in effectively resolving decision-making problems. Recently, Bonferroni meant (BM) operator has received great attention among scholars because of its ability to consider interrelationship among criteria available in decision-making problems. Based on the advantages of the NV and BM operator, we would like to fill in the gaps by developing a Neutrosophic vague normalized weighted Bonferroni mean (NV-NWBM). In addition, five mathematical properties related to proposed AO are also examined. Besides that, a three-phase decision-making framework is presented to clarify that the proposed AO can be applied to real world decision-making issues. The NV-NWBM operator along with decision-making framework is applied to the example of investment selection under NV environment. The finding shows a computer company is the best alternative for investment. Finally, influence of parameter is performed to validate the effect of parameter towards ranking order.

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Hazwani Hashim mail -

Noor Azzah Awang mail -

Lazim Abdullah mail

link https://doi.org/10.54216/IJNS.220107

Volume & Issue

Vol. Volume 22 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

IOT enabled Intelligent featured imaging Bone Fractured Detection System

In the present era, there are lots of advancements and initiatives that have been undertaken through image processing techniques and IoT (Internet of Things). Image processing has proven its valuable insights in various applications such as GIS, biomedical, security, satellite imaging, medicine, and personal image analysis. In the context of fracture detection, image improvements, feature segmentation, and feature extraction techniques are commonly implemented including in the IoT Environment. The lower long bone, hand bone, and elbow bones are the particular interest due to their high incidence of fractures. X-ray diagnosis is a common method of detecting bone fractures due to its rapid and widespread availability. X-ray imaging involves a small amount of ionizing radiation in each part of the body, which is then captured on a particular film or digital detector. X-ray images, though they may have limitations compared to other imaging modalities, provide sufficient quality for fracture detection. There are three points of motivation for this research i.e. First- ease of use of software for patients and reduce the time for doctors and patients by screening out straight forward, Second- to decrease human mistakes that can also occur from manually inspecting a massive dataset of X-ray images to become aware of fractured sections of bones in hospitals, third- use of IoT infrastructure to collecting images of X-Rays and performing processing on received data by which we can send some accurate information back to the patients. The research aims to develop an automated environment i.e IoT emulation Framework consisting of image pre-processing such as attainment of images, pre-post-processing, segment methods, feature extraction, fracture detection, and visualization. Feature Extraction algorithm includes, CLAHE object with the preferred clip limit 2.0, CLAHE to the grayscale image, Gaussian blur to overcome more noise, Canny side detection, Hough Transform for line detection, and the gradient magnitude to acquire binary edges varied out through IoT. The framework utilizes the Canny edge detection methodology and Sobel operator for image segmentation. In this heat maps of images are also observed, which provide accurate information from bone images through IoT. The proposed system illustrates extreme accuracy and effectiveness, as proved by the results acquired from numerous experiments. The automated labeling and detection of bone fractures through photo processing by way of IoT offer great potential for fast and correct diagnosis, contributing to successful treatment outcomes.

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Anita Venugopal mail -

Gajender Kumar mail -

Vinod Patidar mail -

Prolay Biswas mail -

Mukta Patel mail -

Chaur Singh Rajput mail -

Aditi Sharma mail

link https://doi.org/10.54216/JISIoT.090201

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

An Intelligent Multi-Criteria Decision-Making Model for selecting an optimal location for a data center: Case Study in Egypt

For businesses that depend on reliable and secure IT systems, choosing the best location for a data center is of paramount importance. Data center accessibility, operational efficiency, cost, and security are all affected by their physical location. The procedure entails considering a wide range of elements to guarantee that the final site meets the needs of the business. This paper investigated the multi-criteria decision-making (MCDM) model to select the best data center location based on a set of criteria. The MCDM method is integrated with the single-valued neutrosophic set (SVNS) to deal with vague and inaccurate information. A neutrosophic set with truth, indeterminacy, and falsity membership functions all in the range [0, 1] is called a SVNS. This paper used SVNS with three MCDM methods such as entropy, TOPSIS, and MABAC techniques. The entropy technique is used to compute the weights of criteria, then the TOPSIS and MABAC methods are used to rank the locations. The case study is investigated in Egypt. This paper used ten criteria and eight alternatives.

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Alber S. Aziz mail -

Moahmed Emad mail -

Mahmoud Ismail mail -

Heba Rashad mail -

Ahmed M. Ali mail -

Ahmed Abdelhafeez mail -

Shimaa S. Mohamed mail

link https://doi.org/10.54216/JISIoT.090202

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Intelligent Wireless Sensor Networks for Healthcare: Bridging Biomedical Clothing to the IoT Future

The science of achieving a healthy mind, body, and spirit through objectives and activities is known as personal health (PH). We must be aware of our mental, bodily, and social well-being. The term "hygiene" refers to a wide variety of healthy behaviours. Individuals' healthcare costs and quality of life increased by avoiding or reducing the long-term effects of the disease through knowledge and skills. Biomedical apparel includes sutures, vascular grafts, and biodegradable clothes (BC). Biomedical clothing is anything implanted or incorporated into the human body and used near tissue, blood, or cells. Quick, dependable, and energy-efficient connectivity between wireless sensor networks is necessary (WSNs). Physical layers, media access control, networking layers, and control requirements must be co-designed. For those with lesser means, health insurance will increase in cost. There are difficulties with privacy and cyber security, a higher chance of malpractice claims, and increased time and financial expenditures for doctors and patients. In this study, wireless sensor networks-based personal health biomedical clothing (PH-BC-WSN) was utilized to increase access to high-quality healthcare, increase food production through precision agriculture, and raise the standard of human resources. More effective healthcare and medical asset monitoring systems can be developed thanks to the Internet of Things. Eavesdropping on medical data, modification, fabrication of warnings, denial of service, user tracking and location, physical interference with equipment, and electromagnetic threats were extensively discussed. The article gives several instances of current technology, discusses design challenges including energy efficiency, security, and scalability, provides various demonstrations of current technology, and provides a complete analysis of all the advantages and disadvantages. Despite their many benefits, body sensor networks have several significant obstacles and unresolved research problems, which are described along with some potential answers. As a result, the experimental findings demonstrate that PH-BC-WSN enhances accuracy and reduces response time in inpatient health monitoring.

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Sajjad Ali Ettyem mail -

Ibrahem Ahmed mail -

Wasan Saad Ahmed mail -

Naseer Ali Hussien mail -

Maryam Ghassan Majeed mail -

Korhan Cengiz mail -

Narjes Benameur mail

link https://doi.org/10.54216/JISIoT.090203

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Developing a Risk Management System with an Optimistic Predictive Approach and Business Decision-Making

Risk Management is an important task that helps to monitor the business application to eliminate the political, financial, cultural, and social consequences. The organization's risk decision is affected by several characteristics, such as lack of accountability and risk decision-making. The difficulties are resolved by applying the Machine-Learning related Business Decision Making Approach (ML-BDMA). The created framework helps to reduce the difficulties in decision-making while managing the organization's risk. The Business Decision Making process works along with the Optimistic Predictive Techniques (OPT) that are used to identify the risk which leads to attaining the business objective. This process categorizes the risk according to the qualitative characteristics of business data. The system's effectiveness was evaluated using the experimental result in which the system ensures a 98.93% performance rate, 92.25% reliability rate, 93.47% authenticity rate, 91.11% risk management rate, and 97.77% development rate while making a business decision.

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Mustafa Nazar Dawood mail -

Mohammed Ayad Alkhafaji mail -

Ahmed Hussian mail -

Hussein Alaa Diame mail -

Naseer Ali Hussien mail -

Sahar Yassine mail -

Venkatesan Rajinikanth mail

link https://doi.org/10.54216/JISIoT.090204

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

A Framework for Strategic Planning Adaptation in Smart Cities through Recurrent Neural Networks

In the Smart city environment, sustainable sewage and wastewater management planning plays a crucial role in industry development. Wastewater management is a serious issue with inadequate treatment, which reduces the smart city efficiency. Therefore, this research work concentrates on creating the Strategic Planning Adaption framework (SP-AF) using the Recurrent Neural Networks (RNN). This framework intends to manage the sewage and wastewater in smart cities. The sewage-related information is continuously collected by a recurrent network that identifies and tracks the wastewater and sewage in the smart city. The SP-AF framework analyses sustainable planning and managing wastewater by understanding the waste origin. In addition, the framework has been generated by understanding the wastewater knowledge, and the required actions are carried out. Then the effectiveness of the wastewater management system efficiency is compared with the existing approaches.

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Marwa S. Mahdi Hussin mail -

Mohammed Brayyich mail -

Mustafa Al-Tahee mail -

Tamarah A. Diame mail -

Sajad Ali Zearah mail -

Marwan Qaid Mohammed mail -

Salem Saleh Bafjaish mail

link https://doi.org/10.54216/JISIoT.090205

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL)

The development of power industries creates impacts on the intelligent power grids. The power grids are more valuable for transmitting information over the network. Several intermediate activities influence the networks, which are interrupted by traffic, creating network security issues. Therefore, the threats highly influence power grids, and the number of attacks also increased gradually. Several conceptual approaches are introduced to overcome the security issues; however, computation complexity is still a significant problem while detecting network anomalies. This research problem is overcome by applying the Organic Combination of Various Deep Learning (OC-VDL) approach. The introduced method observes the industry standards with the help of the Innovative Blockchain Network (IBN). During this process, IBN observes the infrastructure using the communication protocol and Manufacturing Internet of Things (IoT). The collected information is processed with the help of the Intense Autoencoder Classifier Model (IACM), which manages bilateral traffic control and helps predict abnormal activities. The effective prediction of network traffic minimizes the intermediate activities and improves the overall security up to 98.8% accuracy.

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Tamarah Alaa Diame mail -

Kadim A. Jabbar mail -

Ahmed Taha mail -

Naseer Ali Hussien mail -

Sura Rahim Alatba mail -

Mohammed Nasser Al-Mhiqani mail -

Venkatesan Rajinikanth mail

link https://doi.org/10.54216/JISIoT.090206

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Modeling Sports Event Tasks in Augmentative and Alternative Communication Using Deep Learning

Rapid changes in modern technology and sports have impacted society and lifestyle. Augmentative and Alternative Communication (AAC) technology helps to speak and play videos in various sports applications. In the current sports event, AAC's utilization to validate the players' complex moves exclusively has been considered a significant challenge that includes athlete moves in athletics and penalty shots in Soccer. Deep Learning-based Video Segmentation and Video mining (DL-VSVM) with eyeball tracking assistance are proposed to validate the task modeling of sports event video streaming in AAC. The user could select the specific event in the sport and sub-event using eyeball tracking assistance. The AAC is installed with unique icons to identify circumstances. A deep learning-based Sports Task model is created to recognize the required data to be streamed, and the model will help them view the specific sports event they need to watch. The numerical outcomes demonstrate that the suggested DL-VSVM model enhances the segmentation accuracy ratio of 95.3%, tracking ratio of 97.6%, prediction ratio of 98.7%, and reduces the cost function of 5.6% and the error rate of 20.1% compared to other existing models.

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Noora Hani Sherif mail -

Eay Fahidhil mail -

Najlaa Nsrulaah Faris mail -

Hussein Alaa Diame mail -

Raaid Alubady mail -

Seifedine Kadry mail

link https://doi.org/10.54216/JISIoT.090207

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new