Materials Science

Fast and reliable authentication is a crucial requirement of communications networks and has various research challenges in an Internet of Things (IoT) environment. In IoT-based applications, as fast and user-friendly access and high security are required simultaneously, biometric identification of the user, such as the face, iris, or fingerprint, is broadly employed as an authentication approach. Moreover, a so-called multi-factor authentication that combines user identification with other identification information, including token information and device identity, is usedto enhance the authentication security level. This paper proposes a novel twofactor authentication scheme for intelligent communication and control systems by utilizing the watermarking technique to incorporate the mobile device authentication component into the user’s facial recognition image. Our proposed scheme offers user-friendliness while improving user security and privacy and reducing authentication information exchange procedures to provide a secure and lightweight schema in real applications. The proposed scheme’s security advantages are validated using the widely accepted Burrows–Abadi–Needham (BAN) logic and experimentally assessed using the Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator tool. Finally, our experimental results show that the proposed authentication scheme is an innovative solution for a smarthome control system, such as a smart lock door operation.

Current security challenges are made more difficult by the complexity and difficulty of spotting cyberattacks due to the Internet of Things explosive growth in connected devices and apps. Therefore, various sophisticated attack detection techniques have been created to address these issues in recent years. Due to their effectiveness and scalability, machine learning-based intrusion detection systems (IDSs) have increased. However, several factors, such as the characteristics of the training dataset and the training model, affect how well these AI-based systems identify attacks. In particular, the heuristic algorithms (GA, PSO, CSO, FA) optimized by the logistic regression (LR) approach employ it to pick critical features of a dataset and deal with data imbalance problems. This study offers an intrusion detection system (IDS) based on a deep neural network and heuristic algorithms combined with LR to boost the accuracy of attack detections. Our proposed model has a high attack detection rate of up to 99% when testing on the IoT-23 dataset.

Bloom’s Taxonomy (BT) is widely used in educational institutions to produce high-quality exam papers to evaluate students’ knowledge at different cognitive levels. However, manual question labeling takes a long time, and not all evaluators are familiar with BT. The researchers worked to automate the exam question classification process based on BT as a solution. Enhancement in term weighting is one of the ways to increase classification accuracy while working with text data. However, all the past work on the term weighting in exam question classification focused on unsupervised term weighting (USTW) schemes. The supervised term weighting (STW) schemes showed effectiveness in text classification but were not addressed in past studies of exam question classification. As a result, this study focused on the effectiveness of STW in classifying exam questions using BT. Hence, this research performed a comparative analysis between the USTW schemes and STW for exam question classification. The STW schemes used in this study are TF-ICF, TF-IDF-ICF, and TF-IDF-ICSDF, whereas the USTW schemes used for comparison are TF-IDF, ETF-IDF, and TFPOS-IDF. This study used Support Vector Machines (SVM), Na¨ıve Bayes (NB), and Multilayer Perceptron (MLP) to train the model. Accuracy and F1 score were used in this study to evaluate the classification result. The experiment result showed that overall, the STW scheme TF-ICF outperformed all the other schemes, followed by the USTW scheme ETF-IDF. Both the ETF-IDF and TFPOS-IDF outperformed standard TFIDF. The outcome of this study indicates the future research direction where the combination of STW and USTW schemes may increase the Accuracy of BT-based exam question classification.

One of the essential aspects of smart farming and precision agriculture is quickly and accurately identifying diseases. Utilizing plant imaging and recently developed machine learning algorithms, the timely detection of diseases provides many benefits to farmers regarding crop and product quality. Specifically, for farmers in remote areas, disease diagnostics on edge devices is the most effective and optimal method to handle crop damage as quickly as possible. However, the limitations posed by the equipment’s limited resources have reduced the accuracy of disease detection. Consequently, adopting an efficient machine-learning model and decreasing the model size to fit the edge device is an exciting problem that receives significant attention from researchers and developers. This work takes advantage of previous research on deep learning model performance evaluation to present a model that applies to both the Plant-Village laboratory dataset and the Plant-Doc natural-type dataset. The evaluation results indicate that the proposed model is as effective as the current state-of-the-art model. Moreover, due to the quantization technique, the system performance stays the same when the model size is reduced to accommodate the edge device.

In robot-assisted manufacturing or assembly, following a predefined path became a critical aspect. In general, inverse kinematics offers the solution to control the movement of manipulator while following the trajectory. The main problem with the inverse kinematics approach is that inverse kinematics are computationally complex. For a redundant manipulator, this complexity is further increased. Instead of employing inverse kinematics, the complexity can be reduced by using a heuristic algorithm. Therefore, a heuristic-based approach can be used to solve the inverse kinematics of the robot manipulator end effector, guaranteeing that the desired paths are accurately followed. This paper compares the performance of four such heuristic-based approaches to solving the inverse kinematics problem. They are Bat Algorithm (BAT), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). The performance of these algorithms is evaluated based on their ability to accurately follow a predefined trajectory. Extensive simulations show that BAT and GSA outperform PSO and WOA in all aspects considered in this work related to inverse kinematic problems.

In DCNNs, the number of parameters in pointwise convolutions rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Our proposal makes pointwise convolutions parameter efficient via grouping filters into parallel branches or groups, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest interleaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. We applied our improvement to the EfficientNet, DenseNet-BC L100, MobileNet and MobileNet V3 Large architectures. We trained these architectures with the CIFAR-10, CIFAR-100, Cropped-PlantDoc and The Oxford-IIIT Pet datasets. When training from scratch, we obtained similar test accuracies to the original EfficientNet and MobileNet V3 Large architectures while saving up to 90% of the parameters and 63% of the flops.

Examination is one of the common ways to evaluate the students’ cognitive levels in higher education institutions. Exam questions are labeled manually by educators in accordance with Bloom’s taxonomy cognitive domain. To ease the burden of the educators, several past research works have proposed the automated question classification based on Bloom’s taxonomy using the machine learning technique. Feature selection, feature extraction and term weighting are common ways to improve the accuracy of question classification. Commonly used term weighting method in the past work is unsupervised namely TF and TF-IDF. There are several variants of TF and TFIDF and the most optimal variant has yet to be identified in the context of question classification based on BT. Therefore, this paper aims to study the TF, TF-IDF and normalized TF-IDF variants and identify the optimal variant that can enhance the exam question classification accuracy. To investigate the variants two different classifiers were used, which are Support Vector Machine (SVM) and Naïve Bayes. The average accuracies achieved by TF-IDF and normalized TF-IDF variants using SVM classifier were 64.3% and 72.4% respectively, while using Naïve Bayes classifier the average accuracies for TF-IDF and normalized TF-IDF were 61.9% and 63.0% respectively. Generally, the normalized TF-IDF variants outperformed TF and TF-IDF variants in accuracy and F1-measure respectively. Further statistical analysis using t-test and Wilcoxon Signed also shows that the differences in accuracy between normalized TF-IDF and TF, TF-IDF are significant. The findings from this study show that the Normalized TF-IDF3 variant recorded the highest accuracy of 74.0% among normalized TF-IDF variants. Also, the differences in accuracy between Normalized TF-IDF3 and other normalized variants are generally significant, thus the optimal variant is Normalized TF-IDF3. Therefore, the normalized TF-IDF3 variant is important for benchmarking purposes, which can be used to compare with other term weighting techniques in future work.

Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems. The controller is represented by an MLP-type artificial neural network. The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm. An integral-type performance index representing control quality, which is based on closed-loop simulation, is minimised. The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs.

The cooperation between mobile robots is one of the most important topics of interest to researchers, especially in the many areas in which it can be applied. Hunting a moving target with random behavior is an application that requires robust cooperation between several robots in the multi-robot system. This paper proposed a hybrid formation control for hunting a dynamic target which is based on wolves’ hunting behavior in order to search and capture the prey quickly and avoid its escape and Multi Agent Deep Deterministic Policy Gradient (MADDPG) to plan an optimal accessible path to the desired position. The validity and the effectiveness of the proposed formation control are demonstrated with simulation results.

A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices.A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices.    Three existing techniques (representations) which allow generating unitary matrices are used in various evolutionary algorithms in order to optimize their coefficients.    The objective is to obtain as precise quantum operators (the resulting unitary matrices) as possible for given quantum transformations.    Ordinary evolution strategy, self-adaptive evolution strategy and differential evolution are applied with various settings as the optimization algorithms for the quantum operators.    These algorithms are evaluated on the tasks of designing quantum operators for the 3-qubit and 4-qubit maximum amplitude detector and a solver of a logic function of three variables in conjunctive normal form.    These tasks require unitary matrices of various sizes.    It will be demonstrated that the self-adaptive evolution strategy and differential evolution are able to produce remarkably better results than the ordinary evolution strategy.    Moreover, the results can be improved by selecting a proper settings for the evolution as presented by a comparative evaluation.

In modern applications, large graphs are usually applied in the simulation and analysis of large complex systems such as social networks, computer networks, maps, traffic networks. Therefore, graph mining is also an interesting subject attracting many researchers. Among them, frequent subgraph mining in a single large graph is one of the most important branches of graph mining, it is defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. In which, the GraMi algorithm is considered the state of the art approach and many algorithms have been proposed to improve this algorithm. In 2020, the SoGraMi algorithm was proposed to optimize the GraMi algorithm and presented an outstanding performance in terms of runtime and storage space. In this paper, we propose a new algorithm to improve SoGraMi based on connected components, called CCGraMi (Connected Components GraMi). Our experiments on four real datasets (both directed and undirected) show that the proposed algorithm outperforms SoGraMi in terms of running time as well as memory requirements.

Computer-aided diagnosis methods are being developed to assist radiologists to improve the interpretation of mammograms for the detection and diagnose of breast cancer, reduce the errors and mistakes made by human beings. In addition, it provides a more accurate and reliable classification of benign and malignant abnormalities. In the mammogram diagnosis, the pectoral muscle appears in Mediolateral oblique views (MLO) of the right and left of the breast. Considering that, the pectoral muscle has the same density as the small suspicious masses in the image and can affect/bias the results of image processing methods. This paper presents a diagnosis method to detect an abnormality in mammograms automatically. Before abnormality identification, image-processing techniques are used to correctly segment the suspicious region-of-interest (ROI). The background of the mammograms has been darkened to distinguish the breast area from any blemishes or writings that will be removed. Then the breast area has been extracted after ignoring the empty regions around the breast in mammogram images. After that, the mammogram image is inverted and the inverted image is then subtracted from the initial image. For pectoral muscle removal, a region growing method using the K-means clustering method is used. Afterward, suspicious ROI is segmented utilizing the K-means with thresholding technique. To detect abnormalities in mammograms, shape-based features, moment invariants, and also fractal dimensions are extracted from the segmented ROI. The Mini-MIAS dataset is used to evaluate the proposed method and is predominately composed of benign samples with only a tiny percent of malignant samples. To accomplish far better classifier efficiency, the SMOTE algorithm is used to present new samples from the minority classes to get a balanced dataset. Random forest classifier utilized to classify the segmented region as benign and malignant. The experimental results obtained an accuracy of 97.1%, the sensitivity of 95.1%, and the achieved specificity is 98.5%.

Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation of the neighborhood size and the rate of diversity loss in the Particle Swarm Optimization algorithm with local topology (also known as LPSO). We argue that the neighborhood size is an important input to consider when designing any adaptive approach based on the change of population diversity. We used the extensive benchmark suite of the IEEE CEC 2014 competition for experiments.

The need to scale software test automation while managing the test automation process within a reasonable time frame remains a crucial challenge for software development teams (DevOps). Unlike hardware, the software cannot wear out but can fail to satisfy the functional requirements it is supposed to meet due to the defects observed during system operation. In this era of big data, DevOps teams can deliver better and efficient code by utilizing machine learning (ML) to scan their new codes and identify test coverage gaps. While still in its infancy, the inclusion of ML in software testing is a reality and requirement for coming industry demands. This study introduces the prospects of robot testing and machine learning to manage the test automation process to guarantee software reliability and quality within a reasonable timeframe. Although this paper does not provide any particular demonstration of ML-based technique and numerical results from ML-based algorithms, it describes the motivation, possibilities, tools, components, and examples required for understanding and implementing the robot test automation process approach.

One of the record killers in the world is lung disease. Lung disease denotes to many disorders affecting the lungs. These diseases can be identified through Chest X- Ray, Computed Tomography CT, Ultrasound tests. This study provides a systematic review on different types of Deep Learning (DL) designs, methods, techniques used by different researchers in diagnosing COVID-19, Pneumonia, Tuberculosis, Lung tumor, etc. In the present research study, a systematic review and analysis is carried by following PRISMA research methodology. For this study, more than 900 research articles are considered from various indexing sources such as Scopus and Web of Science. After several selection steps, finally a 40 quality research articles are included for detailed analysis. From this study, it is observed that majority of the research articles focused on DL techniques with Chest X-Ray images and few articles focused on CT scan images and very few have focused on Ultrasound images to identify the lung disease