Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and $F1$ -measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and $99\%~F1$ -measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and $F1$ Measure score is 80%

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robust control design based on 32-bit microcontroller in a sensed environment (SE). The robot keeps itself balance with two wheels and a PD-PI controller based on the Kalman filter algorithm during the navigation process and is able to stabilize while avoiding acute and dynamic obstacles in the sensed environment. The Proportional (P) control is used to implement turn control for obstacle avoidance in SE with ultrasonic waves. Finally, in a SE, the robot can communicate with any of the Internet of Things (IoT) devices (mobile phone or Personal Computer) which have a Java-based transmission application installed and through Bluetooth technology connectivity for wireless control. The simulation results prove the efficiency of the proposed PD-PI controller in path planning, and balancing challenges of the TWSBR under several environmental disturbances. This shows an improved control system as compared to the existing improved Adaptive Fuzzy Controller

Security of Things intrusion detection system for smart healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precision performance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2

A counter-eavesdropping technique for optimized privacy of wireless industrial IoT communications.

The Industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issues of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this paper is on establishing efficient privacy in an IIoT-MIMOME communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as Optimal Counter-Eavesdropping Channel Approximation (OPCECA) technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise

An Efficient Data Transmission in Smart Grids Using Edge Computing

In the world of smart grids, there has been improvements like protection, efficiency and environmental friendly power systems. With a huge amount of data transmitted through the IOT devices, cloud-only architecture could not hold the delay, throughput and response time of these data via the network. For that reason, the reference architecture with the involvement of the edge computing for smart grid is established. We considered discussing edge computing as an extension of the cloud, assisting the smart meters and IoT devices in smooth data transmission to and fro the entire smart grid. The edge nodes helps to ease load on cloud, improve its performance and efficiency, and also provide real-time calculating service. Performance relating to delay, throughput and response time of cloud services and edge services is shown and evaluated. Our simulation proved that the edge based smart grid architecture has a great improvement over the cloud state of the art technique

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features

Design and implementation of a secure patient recommender and prediction system

Early disease prediction can help sick persons determine the severity of the disease and take quick action, thus, a healthcare recommended system is viewed as additional tools to help patients control and manage their ill-health. Medical recommended system which provides users with quick and optimal disease predictions has been in existence for a while; however, it is faced with several data security issues. Sometimes, patients confidential data which are stored of the archive after each recommendation may be accessed by unauthorized persons, and this can warrant a serve data breach and disclosure of private medical information. Thus, the focus of our project is to design a privacy-aware recommended system that not just makes facilitates quick and easy recommendation for sick persons but also securely protects stored medical information from unauthorized access. This system will be designed to support quick search, recommendation septimal confidentiality and integrit

Design and implementation of a secure patient recommender and prediction system

Early disease prediction can help sick persons determine the severity of the disease and take quick action, thus, a healthcare recommended system is viewed as additional tools to help patients control and manage their ill-health. Medical recommended system which provides users with quick and optimal disease predictions has been in existence for a while; however, it is faced with several data security issues. Sometimes, patients confidential data which are stored of the archive after each recommendation may be accessed by unauthorized persons, and this can warrant a serve data breach and disclosure of private medical information. Thus, the focus of our project is to design a privacy-aware recommended system that not just makes facilitates quick and easy recommendation for sick persons but also securely protects stored medical information from unauthorized access. This system will be designed to support quick search, recommendation septimal confidentiality and integrit

Blockchain-based security mechanism for the medical data at fog computing architecture of internet of things

The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model