Journal of Intelligent Systems and Internet of Things

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2690-6791ISSN (Online) 2769-786XISSN (Print)

Modeling of Dung Beetle Optimization-based Sink Node Localization Approach for Wireless Sensor Networks

R. Padmaraj , K. Selvakumar

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques

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Vol. 13 Issue. 1 PP. 08-20, (2024)

A DEMATEL Analysis of the Complex Barriers Hindering Digitalization Technology Adoption in the Malaysia Agriculture Sector

Zahari Md Rodzi , Nur A. Mat Rosly , Nurul A. Mohd Zaik , Muhammad Hakimi Rusli , Ghafur Ahmad , Faisal Al-Sharqi , Ashraf Al-Quran , Ali M. A. Bany Awad

This study investigates the challenges to the digitalization technology adoption in Malaysia agriculture sector by using the DEMATEL (Decision-Making Trial and Evaluation Laboratory) approach, which will give a complete knowledge of the interdependencies among the barriers. The research objectives are to determine the cause and effect of digital agriculture using DEMATEL and to recommend the best way to overcome the obstacles in using digital technology.  The findings from this study reveals the cause and effect from the barriers which is lack of skills, lack of technology, high cost, infrastructure and connectivity, and resistance to change are in the cause group while limited locality, data privacy and security concerns, low level of education, market access and regulatory and policy are in the effect group.  The research findings are utilized to give policymakers and stakeholders with practical recommendations aimed at addressing the identified barriers and promoting the adoption of digital technologies in Malaysian agriculture.  Thus, this study offers recommendations for the most important obstacles found, which are an improvement in infrastructure and the implementation of financial assistance mechanisms.  All things considered, this research makes a significant contribution to the subject of agriculture and sheds light on the difficulties associated with implementing new technologies in Malaysia's agriculture industry.

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Vol. 13 Issue. 1 PP. 21-30, (2024)

Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management

S. Phani Praveen , Chandra Shikhi Kodete , Saibaba velidi , Srikanth Bhyrapuneni , Suresh Babu Satukumati , Vahiduddin Shariff

Medical care conveyance has been transformed by the Internet of Things (IoT's) combination into wellbeing systems, which provides doctors and patients with continuous on-request services. However, this coordination poses questions with respect to the precision of the information and possible security risks. This research expects to present a sharp character the executives structure planned for IoT and distributed computing based personalized medical care frameworks. The purpose is to upgrade confirmation processes while restricting security threats through the double-dealing of multimodal encoded biometric features. The suggested approach incorporates biometric-based continuous authentication together with combined and concentrated personality access strategies. To safeguard patient information in the cloud, it combines electrocardiogram (ECG) and photoplethysmogram (PPG) signals for authentication, which is further bolstered by homomorphic encryption (HE). An AI (ML) model was used to assess the system's reasonability including a dataset of 20 clients in various seating configurations. The merged based biometric structure defeated standalone ECG or PPG signal-based procedures in perceiving and authenticating every client with 100% exactness. The proposed framework makes significant improvements to the privacy and security of personalized healthcare frameworks. It fulfills the essential security necessities and is by the by viable enough to run on low-end processors. It guarantees trustworthy authentication and protects against conventional security threats by utilizing multimodal biometric features and cutting-edge encryption techniques.

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Vol. 13 Issue. 1 PP. 31-45, (2024)

Enhancing Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning Model on Internet of Things Environment

Naresh Sharma , Rohit Sharma

An optimal solution for monitoring air pollution, the Internet of Things (IoT)-enabled system delivers real-time data and insights on the air quality within a specific location. Air pollution poses a substantial risk to human health worldwide, with pollutants like nitrogen dioxide, particulate matter, ozone, and sulfur dioxide contributing to a range of cardiovascular and respiratory ailments. Monitoring air pollution levels is critical to understand the effect on public health and the environment. Air Pollution Monitoring includes the systematic analysis and measurement of pollutant concentration in the air, through a network of monitoring stations equipped with instruments and sensors. This station provides real-time data on air quality, allowing authorities to evaluate issue warnings, and pollution levels, and implement strategies to alleviate its negative impact. Machine learning (ML) approaches are becoming more integrated into air pollution monitoring systems for enhancing efficiency and accuracy. By analyzing vast quantities of information gathered from satellite imagery, monitoring stations, and other sources, ML approaches could detect patterns, forecast pollution levels, and pinpoint sources of pollution. This study introduces Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning (APMP-AVOAML) model in IoT environment. The drive of the APMP-AVOAML methodology is to recognize and classify the air quality levels in the IoT environment. In the APMP-AVOAML technique, a four stage process is encompassed. Firstly, min-max normalization is applied for scaling the input data. Secondly, a harmony search algorithm (HSA) based feature selection process is executed. Thirdly, the extreme gradient boosting (XGBoost) model is utilized for air pollution prediction. Finally, AVOA based parameter selection process is exploited for the XGBoost model. To illustrate the performance of the APMP-AVOAML algorithm, a brief experimental study is made. The resultant outcomes inferred that the APMP-AVOAML methodology has resulted in effectual outcome.

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Vol. 13 Issue. 1 PP. 46-58, (2024)

Intelligent System for the Classification of Arterial Blood Pressure Waveform Abnormalities Due to Mistiming in Intra-Aortic Balloon Pump

Zainab A. Wajeeh , Sadiq J. Hamandi , Wisam S. Alobaidi

Cardiovascular diseases detection or diagnosis on appropriate time is crucial to avoid health complications. In this study, an advanced procedure for classifying changes in the blood pressure has been used analyzing the wave-forms inside the arterial system where such variation can occur due to improper timing in intra-aortic balloon pump (IABP) control. Inaccurate pressure extends with probable injury can be caused by improper timing in the heart valve in both pumping and compression of the balloon. This investigation focuses on accurately recognizing and classifying any irregularities in the artery wave-forms in IABP in the blood pressure initiated by mistiming. Accumulated blood pressure records are used for the progression of providing information to IABP trainer. The wave-forms require pre-handling employing image digitizing software to acquire automated identifications. Any undesirable image features have been removed using Wavelet in MATLAB software. Afterward, such features can be employed to develop a technique for arrangement depending on neural networks. The artificial neural network technique has used marked data to properly detect irregularities in wave-forms in vascular blood pressure due to improper IABP timing. As a result, the validation has proved to appropriately recognize and classify such anomalies, denoting a considerable prospect to improve patient protection with an efficacy of treatment in the area of cardiovascular prescription.

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Vol. 13 Issue. 1 PP. 59-70, (2024)

An Improved Internet of Thing-based Optimized SVM Approach for ECG-founded Cardiac Arrhythmia Classification

Yogendra Narayan Prajapati , Beemkumar N. , Mary Christeena Thomas , Lovish Dhingra , Rishabh Bhardwaj , Aws Zuhair Sameen

Cardiovascular diseases (CVD) stand as the leading cause of global mortality, claiming millions of lives annually. An electrocardiogram (ECG) records the heart's electrical activity based on the Internet of Things (IoT), crucial in detecting cardiac arrhythmias (CA), characterized by irregular heart rates and rhythms. Signals from the MIT-BIH Arrhythmia Physio net database are analyzed. This chapter aims to propose a hybrid approach merging Genetic Algorithm-Support Vector Machine (GSVM) and Particle Swarm Optimization-Support Vector Machine (PSVM) for CA classification. The study introduces an algorithm for categorizing ECG beats into six groups using Independent Component Analysis (ICA)-derived features. Optimal SVM settings are determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on ICA features computed via non-parametric power spectral estimation. The research delves into the origins and methodologies of GA and PSO. Simulation results comparing GSVM and PSVM are presented, emphasizing PSVM's superior performance in accuracy, sensitivity, specificity, and positive predictivity. Detailed performance metrics, including Sensitivity, Specificity, Positive Predictivity, and Accuracy percentages, are scrutinized and compared against the top classifier. The findings endorse PSVM's superiority over GSVM, indicating enhanced performance across multiple evaluation criteria.

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Vol. 13 Issue. 1 PP. 71-82, (2024)