Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

In the Internet of Things (IoT) based system, the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems (UCS). The UCS necessitates heterogeneity, management level, and data transmission for distributed users. Simultaneously, security remains a major issue in the IoT-driven UCS. Besides, energy-limited IoT devices need an effective clustering strategy for optimal energy utilization. The recent developments of explainable artificial intelligence (XAI) concepts can be employed to effectively design intrusion detection systems (IDS) for accomplishing security in UCS. In this view, this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System (BXAI-IDCUCS) model. The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment. The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization (EADSO) algorithm to accomplish this. Besides, deep neural network (DNN) is employed for detecting and classifying intrusions in the IoT network. Lastly, blockchain technology is exploited for secure inter-cluster data transmission processes. To ensure the productive performance of the BXAI-IDCUCS model, a comprehensive experimentation study is applied, and the outcomes are assessed under different aspects. The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%, a packet loss rate of 0.71%, a throughput of 92.95 Mbps, energy consumption of 0.0891 mJ, a lifetime of 3529 rounds, and accuracy of 99.38%.     

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks (CRWSN) can be defined as a promising technology for developing bandwidth-limited applications. CRWSN is widely utilized by future Internet of Things (IoT) applications. Since a promising technology, Cognitive Radio (CR) can be modelled to alleviate the spectrum scarcity issue. Generally, CRWSN has cognitive radio-enabled sensor nodes (SNs), which are energy limited. Hierarchical cluster-related techniques for overall network management can be suitable for the scalability and stability of the network. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN. Besides, the MDMO-EAC algorithm is based on the dwarf mongoose optimization (DMO) algorithm design with oppositional-based learning (OBL) concept for the clustering process, showing the novelty of the work. In addition, the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency. The presented model is validated using a comprehensive range of experiments, and the outcomes were scrutinized in varying measures. The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.     

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for mixed or confusing opinions. The process of analyzing changes in sentiment and the combination of these categories is known as “sentiment analysis.” In this study, sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods. The deep learning-based model long-short-term memory (LSTM) performed better than machine learning approaches. Long short-term memory achieved 87% accuracy, and the support vector machine (SVM) classifier achieved slightly worse results than LSTM at 86%. The study also tested binary classes of positive and negative, where LSTM and SVM both achieved 90% accuracy.     

Investigation of stage-discharge model performance for streamflow estimating: A case study of the Gono River,Japan

In water resource studies, long-term measurements of river streamflow are essential. They allow us to observe trends and natural cycles and are prerequisites for hydraulic and hydrology models. This paper presents a new application of the stage-discharge rating curve model introduced by Maghrebi et al. (2016) to estimate continuous streamflow along the Gono River, Japan. The proposed method, named single stage-discharge (SSD) method, needs only one observed data to estimate the continuous streamflow. However, other similar methods require more than one observational data to fit the curve. The results of the discharge estimation by the SSD are compared with the improved fluvial acoustic tomography system (FATS), conventional rating curve (RC), and flow-area rating curve (FARC). Some statistical indicators, such as the coefficient of determination (R²), root mean square error (RMSE), percent bias (PBAIS), mean absolute error (MAE), and Kling-Gupta efficiency (KGE), are used to assess the performance of the proposed model. ADCP data are used as a benchmark for comparing four studied models. As a result of the comparison, the SSD method outperformed of FATS method. Also, the three studied RC methods were highly accurate at estimating streamflow if all observed data were used in calibration. However, if the observed data in calibration was reduced, the SSD method by R² = 0.99, RMSE = 2.83 (m³/s), PBIAS = 0.715(%), MAE = 2.30 (m³/s), and KGE = 0.972 showed the best performance compared to other methods. It can be summarized that the SSD method is the feasible method in the data-scarce region and delivers a strong potential for streamflow estimation.     

A statistical framework for breast tumor classification from ultrasonic images

Segmentation and classification of ultrasonic breast images is extremely critical for medical diagnosis. Over the last years, various techniques have already been presented for this objective. In this paper, a proposed framework is presented to segment a given ultrasonic image with breast tumor and classify the tumor as being benign or malignant. The proposed framework depends on an active contour segmentation model to determine the tumor region, and then extract it from the ultrasonic image. After that, the Discrete Wavelet Transform (DWT) is used to extract features from the segmented images. Then, the dimensions of the resulting features are reduced by applying feature reduction approaches, namely, the Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA) and both of them together. The obtained features are submitted to a statistical classifier and the strategy of voting is used to classify the tumor. In the simulation work, 160 benign and malignant breast tumor images collected from Sirindhorn International Institute of Technology (SIIT) website are used. The average processing time for a 256 × 256 image on a laptop with Core i5, 2.3 GHz processor and 8GB RAM is 1.8 s. From the simulation results, it is found that the utilization of the PCA approach provides the best accuracy of 99.23% among the three feature reduction approaches applied. Finally, the proposed framework is compared with the Support Vector Machine (SVM) classification to evaluate its performance in terms of accuracy, sensitivity, precision, and specificity. It is noticed that the proposed framework is efficient and rapid, and it can be applied for ultrasonic breast image segmentation and classification, and thus it can assist the specialists to segment and decide whether a tumor is benign or malignant.

     

An optimized FP-growth algorithm for discovery of association rules

The Journal of Supercomputing - Association rule mining (ARM) is a data mining technique to discover interesting associations between datasets. The frequent pattern-growth (FP-growth) is an...     

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R² score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.     

Large-scale channel characterization at 28 GHz on a university campus in the United Arab Emirates

Telecommunication Systems - Millimeter wave (mm-wave) communication is one of the key enabling technologies for meeting the requirements of the fifth generation (5G) wireless communication systems....     

Automated performance‐based design technique for an efficient LTE PDSCH implementation using SDSoC tool

System on a chip (SoC) creates massive design challenges for SoC‐based designers. The design challenges start from functional, architectural verification complexity and finally meeting performance constraints. In addition, heterogeneity of components and tools introduces long design cycles. The Software‐Defined System‐on‐Chip (SDSoC) developed by Xilinx is used to create custom SoC on a heterogeneous FPGA‐CPU platform. The SDSoC tool provides fast, flexible, and short design cycle to develop heterogeneous FPGA‐CPU platform. The objective of this paper is to introduce a new automated design technique to build a SoC on a heterogeneous FPGA‐CPU platform that meets design requirements using SDSoC tool. In this paper, the typical SDSoC design flow is introduced. In addition, a new automated SDSoC design technique is developed to design SoC on a heterogeneous FPGA‐CPU platform on the basis of performance metrics such as area, power, and latency. Design of physical downlink shared channel (PDSCH) in long‐term evolution (LTE) is presented as a case study. This paper provides the implementation of the transmitter and the receiver of the PDSCH in LTE using SDSoC tool and selects a platform that meets performance metrics constraints.