Distributed Rule-Enabled Interworking Architecture Based on the Transparent Rule Proxy in Heterogeneous IoT Networks.

Rule-enabled Internet of Things (IoT) systems operate autonomous and dynamic service scenarios through real-time events and actions based on deployed rules. For handling the increasing events and actions in the IoT networks, the computational ability can be distributed and deployed to the edge of networks. However, operating a consistent rule to provide the same service scenario in heterogeneous IoT networks is difficult because of the difference in the protocols and rule models. In this paper, we propose a transparent rule deployment approach based on the rule translator by integrating the interworking proxy to IoT platforms for operating consistent service scenarios in heterogeneous IoT networks. The rule-enabled IoT architecture is proposed to provide functional blocks in the layers of the client, rule service, IoT service, and device. Additionally, the interworking proxy is used for translating and transferring rules between IoT platforms in different IoT networks. Based on the interactions between the IoT platforms, the same service scenarios are operated in the IoT environment. Moreover, the integrated interworking proxy enables the heterogeneity of IoT frameworks in the IoT platform. Therefore, rules are deployed on IoT platforms transparently, and consistent rules are operated in heterogeneous IoT networks without considering the underlying IoT frameworks.

Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients.

Home appliances are considered to account for a large portion of smart homes' energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It is thought that proper control of these home appliances can reduce significant amounts of energy use. For this purpose, optimization techniques focusing mainly on energy reduction are used. Current optimization techniques somewhat reduce energy use but overlook user convenience, which was the main goal of introducing home appliances. Therefore, there is a need for an optimization method that effectively addresses the trade-off between energy saving and user convenience. Current optimization techniques should include weather metrics other than temperature and humidity to effectively optimize the energy cost of controlling the desired indoor setting of a smart home for the user. This research work involves an optimization technique that addresses the trade-off between energy saving and user convenience, including the use of air pressure, dew point, and wind speed. To test the optimization, a hybrid approach utilizing GWO and PSO was modeled. This work involved enabling proactive energy optimization using appliance energy prediction. An LSTM model was designed to test the appliances' energy predictions. Through predictions and optimized control, smart home appliances could be proactively and effectively controlled. First, we evaluated the RMSE score of the predictive model and found that the proposed model results in low RMSE values. Second, we conducted several simulations and found the proposed optimization results to provide energy cost savings used in appliance control to regulate the desired indoor setting of the smart home. Energy cost reduction goals using the optimization strategies were evaluated for seasonal and monthly patterns of data for result verification. Hence, the proposed work is considered a better candidate solution for proactively optimizing the energy of smart homes.

Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks.

The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification.

Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.

Enhanced Harmonics Reactive Power Control Strategy Based on Multilevel Inverter Using ML-FFNN for Dynamic Power Load Management in Microgrid.

The shift of the world in the past two decades towards renewable energy (RES), due to the continuously decreasing fossil fuel reserves and their bad impact on the environment, has attracted researchers all around the world to improve the efficiency of RES and eliminate problems that arise at the point of common coupling (PCC). Harmonics and un-balance in 3-phase voltages because of dynamic and nonlinear loads cause a lagging power factor due to inductive load, active power losses, and instability at the point of common coupling. This also happens due to a lack of system inertia in micro-grids. Passive filters are used to eliminate harmonics at both the electrical converter's input and output sides and improve the system's power factor. A Synchronous Reference Frame (SRF) control method is used to overcome the problem related to grid synchronization. The sine pulse width modulation (SPWM) technique provides gating signals to the switches of the multilevel inverter. A multi-layer feed forward neural network (ML-FFNN) is employed at the output of a system to minimize mean square error (MSE) by removing the errors between target voltages and reference voltages produced at the output of a trained model. Simulations were performed using MATLAB Simulink to highlight the significance of the proposed research study. The simulation results show that our proposed intelligent control scheme used for the suppression of harmonics compensated for reactive power more effectively than the SRF-based control methods. The simulation-based results confirm that the proposed ML-FFNN-based harmonic and reactive power control technique performs 0.752 better in terms of MAE, 0.52 for the case of MSE, and 0.222 when evaluating based on the RMSE.

Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization.

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring.

Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient's vital signs data in the IoT-based e-Health domain.

Towards Secure Fitness Framework Based on IoT-Enabled Blockchain Network Integrated with Machine Learning Algorithms.

Blockchain technology has recently inspired remarkable attention due to its unique features, such as privacy, accountability, immutability, and anonymity, to name of the few. In contrast, core functionalities of most Internet of Things (IoT) resources make them vulnerable to security threats. The IoT devices, such as smartphones and tablets, have limited capacity in terms of network, computing, and storage, which make them easier for vulnerable threats. Furthermore, a massive amount of data produced by the IoT devices, which is still an open challenge for the existing platforms to process, analyze, and unearth underlying patterns to provide convenience environment. Therefore, a new solution is required to ensure data accountability, improve data privacy and accessibility, and extract hidden patterns and useful knowledge to provide adequate services. In this paper, we present a secure fitness framework that is based on an IoT-enabled blockchain network integrated with machine learning approaches. The proposed framework consists of two modules: a blockchain-based IoT network to provide security and integrity to sensing data as well as an enhanced smart contract enabled relationship and inference engine to discover hidden insights and useful knowledge from IoT and user device network data. The enhanced smart contract aims to support users with a practical application that provides real-time monitoring, control, easy access, and immutable logs of multiple devices that are deployed in several domains. The inference engine module aims to unearth underlying patterns and useful knowledge from IoT environment data, which helps in effective decision making to provide convenient services. For experimental analysis, we implement an intelligent fitness service that is based on an enhanced smart contract enabled relationship and inference engine as a case study where several IoT fitness devices are used to securely acquire user personalized fitness data. Furthermore, a real-time inference engine investigates user personalized data to discover useful knowledge and hidden insights. Based on inference engine knowledge, a recommendation model is developed to recommend a daily and monthly diet, as well as a workout plan for better and improved body shape. The recommendation model aims to facilitate a trainer formulating effective future decisions of trainee's health in terms of a diet and workout plan. Lastly, for performance analysis, we have used Hyperledger Caliper to access the system performance in terms of latency, throughput, resource utilization, and varying orderer and peers nodes. The analysis results imply that the design architecture is applicable for resource-constrained IoT blockchain platform and it is extensible for different IoT scenarios.

Dynamic Inference Approach Based on Rules Engine in Intelligent Edge Computing for Building Environment Control.

Computation offloading enables intensive computational tasks in edge computing to be separated into multiple computing resources of the server to overcome hardware limitations. Deep learning derives the inference approach based on the learning approach with a volume of data using a sufficient computing resource. However, deploying the domain-specific inference approaches to edge computing provides intelligent services close to the edge of the networks. In this paper, we propose intelligent edge computing by providing a dynamic inference approach for building environment control. The dynamic inference approach is provided based on the rules engine that is deployed on the edge gateway to select an inference function by the triggered rule. The edge gateway is deployed in the entry of a network edge and provides comprehensive functions, including device management, device proxy, client service, intelligent service and rules engine. The functions are provided by microservices provider modules that enable flexibility, extensibility and light weight for offloading domain-specific solutions to the edge gateway. Additionally, the intelligent services can be updated through offloading the microservices provider module with the inference models. Then, using the rules engine, the edge gateway operates an intelligent scenario based on the deployed rule profile by requesting the inference model of the intelligent service provider. The inference models are derived by training the building user data with the deep learning model using the edge server, which provides a high-performance computing resource. The intelligent service provider includes inference models and provides intelligent functions in the edge gateway using a constrained hardware resource based on microservices. Moreover, for bridging the Internet of Things (IoT) device network to the Internet, the gateway provides device management and proxy to enable device access to web clients.

Blockchain-Based Authentication in Internet of Vehicles: A Survey.

Internet of Vehicles (IoV) has emerged as an advancement over the traditional Vehicular Ad-hoc Networks (VANETs) towards achieving a more efficient intelligent transportation system that is capable of providing various intelligent services and supporting different applications for the drivers and passengers on roads. In order for the IoV and VANETs environments to be able to offer such beneficial road services, huge amounts of data are generated and exchanged among the different communicated entities in these vehicular networks wirelessly via open channels, which could attract the adversaries and threaten the network with several possible types of security attacks. In this survey, we target the authentication part of the security system while highlighting the efficiency of blockchains in the IoV and VANETs environments. First, a detailed background on IoV and blockchain is provided, followed by a wide range of security requirements, challenges, and possible attacks in vehicular networks. Then, a more focused review is provided on the recent blockchain-based authentication schemes in IoV and VANETs with a detailed comparative study in terms of techniques used, network models, evaluation tools, and attacks counteracted. Lastly, some future challenges for IoV security are discussed that are necessary to be addressed in the upcoming research.

Design and Analysis of In-Pipe Hydro-Turbine for an Optimized Nearly Zero Energy Building.

Pakistan receives Direct Normal Irradiation (DNI) exceeding 2000 kWh/m²/annum on approximately 83% of its land, which is very suitable for photovoltaic production. This energy can be easily utilized in conjunction with other renewable energy resources to meet the energy demands and reduce the carbon footprint of the country. In this research, a hybrid renewable energy solution based on a nearly Zero Energy Building (nZEB) model is proposed for a university facility. The building in consideration has a continuous flow of water through its water delivery vertical pipelines. A horizontal-axis spherical helical turbine is designed in SolidWorks and is analyzed through a computational fluid dynamics (CFD) analysis in ANSYS Fluent 18.1 based on the K-epsilon turbulent model. Results obtained from ANSYS Fluent have shown that a 24 feet vertical channel with a water flow of 0.2309 m3/s and velocity of 12.66 m/s can run the designed hydroelectric turbine, delivering 168 W of mechanical power at 250 r.p.m. Based on the turbine, a hybrid renewable energy system (HRES) comprising photovoltaic and hydroelectric power is modelled and analyzed in HOMER Pro software. Among different architectures, it was found that architecture with hydroelectric and photovoltaic energy provided the best COE of $0.09418.

Reliable Task Management Based on a Smart Contract for Runtime Verification of Sensing and Actuating Tasks in IoT Environments.

With the gradual popularization of Internet-of-Things (IoT) applications and the development of wireless networking technologies, the use of heterogeneous devices and runtime verification of task fulfillment with different constraints are required in real-world IoT scenarios. As far as IoT systems are concerned, most of them are built on centralized architectures, which reveal various assailable points in data security and privacy threats. Hence, this paper aims to investigate these issues by delegating the responsibility of a verification monitor from a centralized architecture to a decentralized manner using blockchain technology. We present a smart contract-based task management scheme to provide runtime verification of device behaviors and allows trustworthy access control to these devices. The business logic of the proposed system is specified by the smart contract, which automates all time-consuming processes cryptographically and correctly. The usability of the proposed solution is further demonstrated by implementing a prototype application in which the Hyperledger Fabric is utilized to implement the business logic for runtime verification and access control with one desktop and one Raspberry Pi. A comprehensive evaluation experiment is conducted, and the results indicate the effectiveness and efficiency of the proposed system.

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation.

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

Towards a Remote Monitoring of Patient Vital Signs Based on IoT-Based Blockchain Integrity Management Platforms in Smart Hospitals.

Over the past several years, many healthcare applications have been developed to enhancethe healthcare industry. Recent advancements in information technology and blockchain technologyhave revolutionized electronic healthcare research and industry. The innovation of miniaturizedhealthcare sensors for monitoring patient vital signs has improved and secured the human healthcaresystem. The increase in portable health devices has enhanced the quality of health-monitoringstatus both at an activity/fitness level for self-health tracking and at a medical level, providing moredata to clinicians with potential for earlier diagnosis and guidance of treatment. When sharingpersonal medical information, data security and comfort are essential requirements for interactionwith and collection of electronic medical records. However, it is hard for current systems to meetthese requirements because they have inconsistent security policies and access control structures.The new solutions should be directed towards improving data access, and should be managed bythe government in terms of privacy and security requirements to ensure the reliability of data formedical purposes. Blockchain paves the way for a revolution in the traditional pharmaceuticalindustry and benefits from unique features such as privacy and transparency of data. In this paper,we propose a novel platform for monitoring patient vital signs using smart contracts based onblockchain. The proposed system is designed and developed using hyperledger fabric, which isan enterprise-distributed ledger framework for developing blockchain-based applications. Thisapproach provides several benefits to the patients, such as an extensive, immutable history log, andglobal access to medical information from anywhere at any time. The Libelium e-Health toolkitis used to acquire physiological data. The performance of the designed and developed system isevaluated in terms of transaction per second, transaction latency, and resource utilization usinga standard benchmark tool known as Hyperledger Caliper. It is found that the proposed systemoutperforms the traditional health care system for monitoring patient data.

Design and Implementation of an Integrated IoT Blockchain Platform for Sensing Data Integrity.

With the rapid development of communication technologies, the Internet of Things (IoT) is getting out of its infancy, into full maturity, and tends to be developed in an explosively rapid way, with more and more data transmitted and processed. As a result, the ability to manage devices deployed worldwide has been given more and advanced requirements in practical application performances. Most existing IoT platforms are highly centralized architectures, which suffer from various technical limitations, such as a cyber-attack and single point of failure. A new solution direction is essential to enhance data accessing, while regulating it with government mandates in privacy and security. In this paper, we propose an integrated IoT platform using blockchain technology to guarantee sensing data integrity. The aim of this platform is to afford the device owner a practical application that provides a comprehensive, immutable log and allows easy access to their devices deployed in different domains. It also provides characteristics of general IoT systems, allows for real-time monitoring, and control between the end user and device. The business logic of the application is defined by the smart contract, which contains rules and conditions. The proposed approach is backed by a proof of concept implementation in realistic IoT scenarios, utilizing Raspberry Pi devices and a permissioned network called Hyperledger Fabric. Lastly, a benchmark study using various performance metrics is made to highlight the significance of the proposed work. The analysis results indicate that the designed platform is suitable for the resource-constrained IoT architecture and is scalable to be extended in various IoT scenarios.

Microservice Security Agent Based On API Gateway in Edge Computing.

Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and privacy issues. Thus, services that protect privacy and secure data are essential functions in edge computing. For example, the end user's ownership and privacy information and control are separated, which can easily lead to data leakage, unauthorized data manipulation, and other data security concerns. Thus, the confidentiality and integrity of the data cannot be guaranteed and, so, more secure authentication and access mechanisms are required to ensure that the microservices are exposed only to authorized users. In this paper, we propose a microservice security agent to integrate the edge computing platform with the API gateway technology for presenting a secure authentication mechanism. The aim of this platform is to afford edge computing clients a practical application which provides user authentication and allows JSON Web Token (JWT)-based secure access to the services of edge computing. To integrate the edge computing platform with the API gateway, we implement a microservice security agent based on the open-source Kong in the EdgeX Foundry framework. Also to provide an easy-to-use approach with Kong, we implement REST APIs for generating new consumers, registering services, configuring access controls. Finally, the usability of the proposed approach is demonstrated by evaluating the round trip time (RTT). The results demonstrate the efficiency of the system and its suitability for real-world applications.

Erratum: Ahmad, S.; Malik, S.; Park, D.-H.; Kim, D. Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles.

The authors wish to make the following erratum to this paper [...].

Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles.

Electric-vehicle technology is an emerging area offering several benefits such as economy due to low running costs. Electric vehicles can also help to significantly reduce CO 2 emission, which is a vital factor for environmental pollution. Modern vehicles are equipped with driver-assistance systems that facilitate drivers by offloading some of the tasks a driver does while driving. Human beings are prone to errors. Therefore, accidents and fatalities can happen if the driver fails to perform a particular task within the deadline. In electric vehicles, the focus has always been to optimize the power and battery life, and thus, any additional hardware can affect their battery life significantly. In this paper, the design of driver-assistance systems has been introduced to automate and assist in some of the vital tasks, such as a braking system, in an optimized manner. We revamp the idea of the traditional driver-assistance system and propose a generic lightweight system based on the leading factors and their impact on accidents. We model tasks for these factors and simulate a low-cost driver-assistance system in a real-time context, where these scenarios are investigated and tasks schedulability is formally proved before deploying them in electric vehicles. The proposed driver-assistance system offers many advantages. It decreases the risk of accidents and monitors the safety of driving. If, at some point, the risk index is above a certain threshold, an automated control algorithm is triggered to reduce it by activating different actuators. At the same time, it is lightweight and does not require any dedicated hardware, which in turn has a significant advantage in terms of battery life. Results show that the proposed system not only is accurate but also has a very negligible effect on energy consumption and battery life.

Development of Virtual Resource Based IoT Proxy for Bridging Heterogeneous Web Services in IoT Networks.

The Internet of Things is comprised of heterogeneous devices, applications, and platforms using multiple communication technologies to connect the Internet for providing seamless services ubiquitously. With the requirement of developing Internet of Things products, many protocols, program libraries, frameworks, and standard specifications have been proposed. Therefore, providing a consistent interface to access services from those environments is difficult. Moreover, bridging the existing web services to sensor and actuator networks is also important for providing Internet of Things services in various industry domains. In this paper, an Internet of Things proxy is proposed that is based on virtual resources to bridge heterogeneous web services from the Internet to the Internet of Things network. The proxy enables clients to have transparent access to Internet of Things devices and web services in the network. The proxy is comprised of server and client to forward messages for different communication environments using the virtual resources which include the server for the message sender and the client for the message receiver. We design the proxy for the Open Connectivity Foundation network where the virtual resources are discovered by the clients as Open Connectivity Foundation resources. The virtual resources represent the resources which expose services in the Internet by web service providers. Although the services are provided by web service providers from the Internet, the client can access services using the consistent communication protocol in the Open Connectivity Foundation network. For discovering the resources to access services, the client also uses the consistent discovery interface to discover the Open Connectivity Foundation devices and virtual resources.

An intelligent diabetes classification and perception framework based on ensemble and deep learning method.

Sugar in the blood can harm individuals and their vital organs, potentially leading to blindness, renal illness, as well as kidney and heart diseases. Globally, diabetic patients face an average annual mortality rate of 38%. This study employs Chi-square, mutual information, and sequential feature selection (SFS) to choose features for training multiple classifiers. These classifiers include an artificial neural network (ANN), a random forest (RF), a gradient boosting (GB) algorithm, Tab-Net, and a support vector machine (SVM). The goal is to predict the onset of diabetes at an earlier age. The classifier, developed based on the selected features, aims to enable early diagnosis of diabetes. The PIMA and early-risk diabetes datasets serve as test subjects for the developed system. The feature selection technique is then applied to focus on the most important and relevant features for model training. The experiment findings conclude that the ANN exhibited a spectacular performance in terms of accuracy on the PIMA dataset, achieving a remarkable accuracy rate of 99.35%. The second experiment, conducted on the early diabetes risk dataset using selected features, revealed that RF achieved an accuracy of 99.36%. Based on our experimental results, it can be concluded that our suggested method significantly outperformed baseline machine learning algorithms already employed for diabetes prediction on both datasets.