Athlete’s Performance Analysis Based on Improved Machine Learning Approach on Wearable Devices

Maryam Ghassan; Hawraa Ali; Mustafa Nazar; Mohaned; Noor Hanoon; Mariok; Ahmed Mollah

doi:https://doi.org/10.54216/JISIoT.080108

Journal of Intelligent Systems and Internet of Things | 2023

Machine Learning Based Logistic Decision Support System for Intelligent Vehicles and Transportation Systems

Recognition and modelling of driver behavior (DB) have lately been crucial in intelligence transportation systems (ITS), human-vehicle, and intelligent vehicle systems (IVS). The evidence that drivers are distracted most often causes accidents and incidents involving vehicles is growing. Using camera sensors in the vehicle or sensors worn by the driver can help detect and prevent drivers from engaging in distracting behaviors like talking on the phone, eating, drinking, adjusting the radio, interacting with navigation systems, or even combing their hair while behind the wheel. However, this system requires a lightweight data processing module and a powerful training module for real-time detection. Data must be collected from certain cameras or wearable sensors to detect distracted drivers and ensure a rapid reaction from the administrator on safe driving. Therefore, this paper suggests a Machine Learning Driver Distraction Prediction Model (MLDDPM) with a decision-support system (DSS) that can alert the driver to possible dangers on the road by analyzing both internal (vehicle parameters) and external (road infrastructure messages) data. This research MLDDPM employed semi-supervised algorithms to reduce the expense of labelling training data for driver attention detection in actual driving scenarios. Two attentive and cognitively distracted driving states were used to assess support vector machines: i) as a supplementary parameter for the aggregate risk assessment of driving and ii) as a parameter for providing the driver with the most appropriate message type on possible road dangers. Finding the optimal approach to driver assistance to guarantee secure transportation is the primary goal of this work.

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Hussein Alaa Diame mail -

Waleed Hameed mail -

Zainab.R.Abdulsada mail -

Noora Hani Sherif mail -

Noor Hanoon Haroon mail -

Narjes Benameur mail -

M. A. Burhanuddin mail

link https://doi.org/10.54216/JISIoT.090208

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Path Scheduling and Bandwidth Utilization for Urban Vehicular Adhoc Networks

Vehicular ad-hoc network (VANETs) is a promising technology that is used in the maximum of the applications of intelligent transport systems (ITS). VANETs become more attractive due to their communication methods such as vehicle-to-vehicle (V2V) and vehicle-to-roadside unit (RSU) communication. VANETs consist of a few special features such as unpredictable mobility, dynamic inter-vehicle spacing, high speed and so on which make communication ineffective. These features network delay and routing overhead increased which affects the stability and reliability of the network. In this paper Path Scheduling and Bandwidth Utilization for VANETs (PSBU-VANETs) are proposed. Through the path scheduling process, the changing topologies are predicted that the prediction path is scheduled for data transmission which leads to reduce the delay and overhead of the network. Through the effective utilization of bandwidth, the throughput and delivery rate of the network are increased. The simulation is performed in NS2 and SUMO and to measure the outcome the parameters which are considered are packet delivery ratio, end-to-end delay, routing overhead, and throughput. To perform a comparative analysis the results of the proposed PSBU-VANETs are compared with the earlier research works such as TDG-VANETs and ICB-VANETs. The proposed PSBU-VANETs achieve a high packet delivery ratio and throughput as well as lower end-to-end delay and routing overhead when compared with the earlier approaches.

groups

Firas Abedi mail -

Osama Yaseen M. Al-Rawi mail -

H. R. Alkhayyat mail -

Rabei Raad Ali mail -

Mohammed Almohamadi mail -

Fatima Hashim Abbas mail -

Wisam Subhi Al-Dayyeni mail

link https://doi.org/10.54216/JISIoT.090209

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Developing Heart Rate Monitoring system for Athletes using Fuzzy Clustering Approach

Athletes health monitoring plays a vital role because the changes in their heart rate reduce their physical activity and contribution. The changes in athlete activities cause developing risk that affects their outcome. Therefore, athletes' heart rates should be monitored frequently to minimize the risk factors and improve their health. This work uses wearable sensor devices to monitor their health condition continuously. The wearable devices on their health record their Electrocardiogram (ECG), which is transferred to the health care centre. With the help of the ECG, this work Sportsperson Heart Rate Monitoring (HRMS-SP) is created. The gathered ECG information is processed using the Fuzzy Clustering (FC) algorithm to predict the Heart Rate Variability (HRV). According to the HRV value, athlete's mental stress level and their sports contribution were also investigated to minimize the computation complexity. In addition, the wearable device-based collected information was investigated using the fuzzy and big data analytics used to monitor people frequently. The predicted information is used to monitor, treat, prevent, and predict the sports person's activities effectively. During the analysis, Hadoop, Visualization, and data mining processes are applied to extract the health information from large datasets that are used to improve the athlete health monitoring systems.

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Laith Fouad mail -

Mazin Riyadh AL-Hameed mail -

Laith S. Ismail mail -

Sajad Ali Zearah mail -

Maryam Ghassan Majeed mail -

Mohd K. Abd Ghani mail -

Hatıra Günerhan mail

link https://doi.org/10.54216/JISIoT.090210

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Intelligent Classification for Credit Scoring Based on a Data Mining algorithm

Credit scoring has grown in importance and has been thoroughly researched by banks and financial institutions. The amount of redundant and irrelevant features present in credit scoring datasets, however, reduces the classification accuracy. As a result, employing effective feature selection methods has become essential. In this study, a hybrid feature selection approach that combines the backpropagation neural network (BPNN) classifier and the pigeon optimization algorithm (POA) is suggested. With hybridization, the POA works to choose characteristic subgroups through the feature selection (FS) process, and the BPNN then assesses the chosen subsets using a fitness function. The experiment findings show that the suggested hybrid technique outperforms other competing approaches in terms of evaluation criteria, according to three benchmark credit scoring datasets.

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Mohammed G. Fathi Al-Obaidi mail

link https://doi.org/10.54216/JISIoT.090211

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Diagnosis of Overlapping Communities and Coherent Groups Using Structural Centrality based Methodology

Community detection in complex networks has become an important step in understanding the structure and behaviour of networks in many fields. However, both standard node clustering and the relatively new link clustering methods have problems that make it hard to find combined clusters. Networks have been used to depict many types of real-world systems, such as those involving the transmission of information, the movement of funds, and biological processes. Communities are key structures for comprehending the structure of actual networks. The purpose of community detection is to identify meaningful subsets of these networks. Mesoscopically, a community consists of highly interconnected nodes within each subcommunity yet less strong connections across subcommunities. Communities can share a node or numerous nodes with overlapping. Evaluating the performance of a community detection method is crucial. Grouping the network's nodes into a family of subsets called clusters such that each cluster comprises similar nodes concerning the overall network structure is the problem of detecting overlapping communities in a network. Meanwhile, it has been shown that many methods for finding cluster centers have inherent flaws. Most methods are vulnerable to initial seeding and built-in variables, while others fail to highlight substantial overlaps. This article proposes the Structural Centrality Approach for Local Overlapping Community Detection (SCA-LOCD). It provides a novel approach to regional development that emphasizes the role of systems in identifying cluster centers. The fundamental concept behind this strategy is to identify structural centers in societies with coherent structures and then increase these centers using weighted methods and search engine techniques. Experimental results on synthetic and network systems show that the suggested technique is efficient and fascinating for detecting overlapped communities. It shows the success of regional extension strategies in identifying coherent groups and producing reliable classification results.

groups

Tamarah Alaa Diame mail -

Sajad Ali Zearah mail -

Sahar R. Abdul Kadeem mail -

Hiba Abdulameer Hasan mail -

Munqith Saleem mail -

Narjes Benameur mail -

M. A. Burhanuddin mail

link https://doi.org/10.54216/JISIoT.090213

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Developing a Smart Economy Using Statistical Framework-Based Business Models in Smart Cities

A smart city's smart economy thrives in various areas, including political strategy, operational efficiency, and innovation management. Business models in smart urban must be based on a new sustainable development strategy, one that conserves natural resources while safeguarding the environment. Therefore, this paper proposes Statistical Business Models (SBM) to enhance the business strategies for developing the economy in smart cities. Economic status in smart cities and changes in business models are part of SBM, a set of design concepts. Smart Business Models (SBM) are business strategies that take advantage of current economic situations by leveraging the power of influential smart communities. The implementation of data systems and business models is the foundation for a systematic study of managing the economy in a smart city. There are several connections between SDM's critical assessments of business models and the global economy and the business models. The experimental findings suggest that the proposed SBM achieves the highest statistical rate with sales revenue up to 95.23 %, gross margin ratio of 80.5%, consumer satisfaction ratio of 96.34%, efficiency ratio of 93.82%, and maintenance cost ratio of 15.08% compared to another existing method.

groups

Noor Hanoon Haroon mail -

Hanan Burhan Saadon mail -

Ansam Mohammed Abed mail -

Ahmed Taha mail -

Maryam Ghassan Majeed mail -

Marwan Qaid Mohammed mail -

Salem Saleh Bafjaish mail

link https://doi.org/10.54216/JISIoT.090214

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Intelligent system for Distributed Quality Monitoring of Sewage Management based on Wastewater Treatment Procedure and Data Mining

Wastewater treatment procedures (WWTP) rely heavily on accurate forecasting of treatment results to keep oxygenation levels under control. Conventional biochemical mechanism-driven approaches provide poor results, mainly due to complicated and redundant system factors. As sewage treatment operations expand fast, automated operational solutions are needed to achieve this goal. In the research, data mining was used to model the WWTP to predict the outcomes based on input circumstances and the amount of oxygenation provided to the system. Combined Sustainability Research for Wastewater Treatment procedures (CSR-WWTP) is proposed in this research. Data-driven approaches to modeling WWTP have already been developed but do not consider long-term treatment procedures and structure features. Forecasting and management for the WWTP are described in this article using a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN). The first stage utilizes the CNN structure to dynamically learn and encrypt the local features of each WWTP timestamp in the first phase. The RNN model is applied to the WWTP to express global sequence characteristics using local feature encryption. For this purpose, it conducts a huge number of tests to assess the performance and accuracy of the proposed forecasting framework.

groups

Shaid Sheel mail -

Sarmad Jaafar Naser mail -

Hussein Alaa Diame mail -

Noor Baqir Hassan mail -

Naseer Ali Hussien mail -

Seifedine Kadry mail

link https://doi.org/10.54216/JISIoT.090215

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Data Management and Decision-Making Process Using Machine Learning Approach for Enterprises

Currently, Machine Learning (ML) seems very attractive since it may speed up business functions in enterprises, lower costs for supplying goods and services, and manage information to promote enterprise efficiency. Essential technological domains nowadays are the explosive period of growth in enterprise solutions, which are progressively used in almost all business platforms. The ML sessions will receive a thorough summary, and the relevant organizations will be shown procedures for relevant business processes. The data management unit is already been striving to solve related issues in ML applications for more than a generation, creating numerous customized analytical techniques. The approach described in the study uses a weighted directed graph displayed in an industrial environment to identify the core part of the neural network structure and then trains them using the relevant data source. The article proposed ML-assisted Enterprise Data Management (ML-EDM) for identifying the trade-off between ML growth in the financial sector and its consequences in precision and interpretability. According to the experimental findings, the ratio of AI for decision-making is 84.25%, the Speed and Agility proportion is 92.70%, the result of Earlier Prediction Management is 93.80%, the  Infrastructure Development is 85.46%, with Data Efficiency is 84.5% and Performance efficiency of the system is 90.14%.

groups

Tamarah Alaa Diame mail -

M. Abdul Jaleel M. mail -

Sajad Ali Ettyem mail -

Raaid Alubady mail -

Mohaned Adile mail -

Mohd K. Abd Ghani mail -

Hatıra Günerhan mail

link https://doi.org/10.54216/JISIoT.080107

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2023

Chili Leaf Disease Detection Using Deep Feature Extraction

Diseases in crops lead to decreased production, which can be addressed through consistent surveillance. Manual surveillance of crop diseases is both arduous and prone to mistakes. The timely identification of crop leaf diseases using Computer Vision and Artificial Intelligence can aid in minimizing the negative impact of diseases and address the limitations of continuous human surveillance.  To classify chili crop diseases, this research paper introduces a new deep feature extraction model based on Transfer Learning using ResNet50, MobileNet, EfficientNetB0, and multiple classifiers. On Plant Village dataset related to the diseases of the chili crop and Private data set, the proposed method was trained and tested. And also analyzed the results by comparing the performance of the pre-trained deep learning models on original data and data filtered through the Image filtering mechanisms and proposed method on the plant village dataset and private dataset, the highest performance accuracy is 99.6% with ResNet50 and the faster CPU time for feature extraction is 29.3 seconds using MobileNet. Comparing the suggested model to the most advanced deep learning models reveals greater accuracy with fewer computational resources.

groups

Pallepati Vasavi mail -

A. Punitha mail -

T. VenkatNarayana Rao mail

link https://doi.org/10.54216/JISIoT.090216

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Athlete’s Performance Analysis Based on Improved Machine Learning Approach on Wearable Devices

Today, every nation strives for international recognition in a variety of sports. Governments invest in games and sports to raise the performance of their teams and athletes to get notoriety. Numerous people are involved in sports execution, including team management, coaches, and biomechanists who monitor athlete fitness and work to achieve remarkable results. Performance analysis is greatly aided by technological integration in sports management. The performance analysis of athletes is evaluated in this research using an upgraded machine learning approach on Improved Machine Learning approach on Wearable Devices (IMLA-WD). This design strategy utilizes wearable devices to collect health data, which is then fed into a machine-learning model to monitor athletes' progress. The athletes' performance is evaluated using standard machine learning methods, and the deep neural network monitors their health status. With a health prediction accuracy of 98.65%, the statistical findings of the proposed model demonstrate the highest performance compared to existing methodologies.

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Maryam Ghassan Majeed mail -

Hawraa Ali Sabah mail -

Mustafa Nazar Dawood mail -

Mohaned Adile mail -

Noor Hanoon Haroon mail -

Mariok Jojoal mail -

Ahmed Mollah Khan mail

link https://doi.org/10.54216/JISIoT.080108

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new