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According to the world health organization report, brain cancer has the highest death rate. magnetic resonance imaging (MRI) for detecting brain tumours is adopted these days due to several advantages over other detection techniques. This paper presents a novel methodology to classify MR images based on texture and deep features, z‐score normalization, and, Comprehensive learning elephant herding optimization (CLEHO) based feature optimization and classification. Deep features of brain MR images have been extracted through DenseNet121 convolutional neural network and texture features have been extracted by using the Gabor 2D filter, Haralick texture feature, edge continuity texture feature, first order statistical texture feature, local binary pattern feature, difference theoretic texture feature, and spectral texture feature techniques. Normalization has been done using three normalization techniques that is, z score, mean median absolute deviation (MMAD), and Tanh‐based after aggregating the features extracted from the previous step. z‐score normalization has been suggested for feature normalization after comparing the results attained from the three techniques. Lastly, binary CLEHO has been proposed for selecting an optimal feature set and also optimizing the 'k' value of the k‐NN classifier. The outcome of this proposed work is compared with other state‐of‐the‐art methods for a publicly available magnetic resonance image Fighshare dataset of 3064 slices from 233 patients. The proposed work has a brain tumour average classification accuracy of 98.97%, which is better than the other state‐of‐the‐art methods. The proposed work can be used to assist the radiologist in the screening of multi‐class brain tumours. [ABSTRACT FROM AUTHOR]

A phoneme is perceptually the smallest distinct sound unit distinguished among words in a particular language. Every language has its own set of phonemes, and all the words are ordered sequences of phonemes. Therefore, phoneme recognition is essential to automatic speech recognition (ASR) systems. Phonemes of a language can be classified together using a single machine learning (ML) model through the direct classification (also known as the baseline or flat classification) approach. However, it is observed that the performance of such phoneme recognition degrades with the increase in the number of phoneme classes. The challenge is pronounced in languages with a larger number of phoneme classes, like Hindi, which has 48 phonemes. In this paper, we propose a speaker‐independent hierarchical classification approach for 33 Hindi‐Devanagari consonants/phonemes using cepstral features with ML techniques like support vector machine (SVM), random forest (RF) and fully connected deep neural network (DNN). In this hierarchical approach, a given phoneme is classified into successive subgroups until the particular phoneme class is identified. To perform the classification task, a binary or multi‐class classifier is invoked for each internal (non‐leaf) node in the hierarchy tree. Our model identified pairs of Optimal Feature Sets (based on mutual information) and the best suitable ML classifier for each internal decision node in the hierarchy using 10‐fold cross‐validation to help in efficient classification. Our proposed hierarchical model leads to better accuracy and 57% improved performance for phoneme recognition compared with the non‐hierarchical, that is, the direct classification approach. [ABSTRACT FROM AUTHOR]

Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo‐anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta‐learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is a common condition affecting women worldwide. In this paper, we analyse physical and mental health factors affecting PCOS in women. We collected data in the form of questionnaires from women belonging to diverse backgrounds and performed a sensitivity analysis to find which factors are significant in determining the presence or absence of PCOS in women and further predict the severity of the symptoms. We implemented four types of methods for determining significant physical and mental health factors affecting the presence and degree of PCOS in women. Our analysis showed that menstrual period cycle length, menstrual period duration, regularity of menstrual periods, menstrual period flow, hair growth, eating pattern, and sleep pattern are the most significant physical health factors and feeling of worthlessness, depression, feeling of hopelessness, not able to be calm, sad and can't cheer up, can't sit still and restlessness are the most significant mental health factors in determining the presence or absence of PCOS in an individual, as well as the degree of PCOS. Furthermore, physical factors are more significant than mental health factors in determining PCOS presence and degree in the cohort of female individuals considered for the analysis. [ABSTRACT FROM AUTHOR]

The COVID‐19 epidemic accelerated the digital change of several services, including healthcare, and increased access to telemedicine. As a result, an increasing number of web tools were introduced to meet patient needs. A safe database can be created in the healthcare industry as a result of digital transformation. This database can be used to protect, store, and share private patient data with healthcare workers, labs, and medical specialists. Designing efficient decision‐making tools for COVID‐19 diagnostics is now possible thanks to recent developments in information technology and deep learning (DL) models. In this paper, a novel method for diagnosing COVID‐19 using deep learning‐enhanced eagle perching optimizer (DTH‐EPODL) model is presented. With the help of the IoT and the presented DTH‐EPODL model, patient information can be gathered and analysed for illness detection. The DTH‐EPODL model uses the Gaussian filtering (GF) method to remove noise in the initial step. Additionally, MixNet, a deep convolutional neural network‐based method, is used for feature extraction. Using the deep autoencoder (DAE) algorithm, COVID‐19 detection and categorization are accomplished. Finally, the DAE approach's associated hyperparameters can be best adjusted using the EPO method, which enhances categorization results. Benchmark chest x‐ray datasets can be used to evaluate the experimental validity of the DTH‐EPODL method. The experimental results showed that the DTH‐EPODL technique outperformed more modern methods. [ABSTRACT FROM AUTHOR]

The progress of deep learning architectures, machine learning models and pathology slide digitization is an encouraging step toward meeting the growing demand for more precise classification and prediction diagnosis for the breast tumours. The BreakHis dataset with four magnification factors (40X, 100X, 200X and 400X), as well as seven deep learning architectures used for feature extraction (DenseNet 201, Inception ResNet V2, Inception V3, ResNet 50, MobileNet V2,VGG16 and VGG19), four machine learning models for classification (MLP, SVM, DT, and KNN), and two combination rules (hard and weighted voting) were investigated in this paper to design and evaluate a new proposed approach consisting of building deep hybrid homogenous ensemble. Additionally, the best proposed models were compared to deep stacked, deep bagging, deep boosting, and deep hybrid heterogenous ensemble to choose the best strategy in building deep ensemble learning techniques. The four performance measures accuracy, precision, recall, and F1‐score were used in the empirical evaluations, as well as 5‐fold cross‐validation, the Scott Knott statistical test, and the Borda Count voting method. The results demonstrated the new approach's potential since it outscored both singles and other deep ensemble learning strategies, achieving accuracy values of 98.3% and 97.7% for the MFs 40X, 100X and 200X, 400X, respectively. The empirical results demonstrated that the proposed ensembles are impactful for histopathological breast cancer images classification, and they provided a promising tool to assist pathologists in the diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

The paper proposes an alternative approach to improve the performance of image retrieval. In this work, a framework for image retrieval based on machine learning and semantic retrieval is proposed. In the preprocessing phase, the image is segmented objects by using Graph‐cut, and the feature vectors of objects presented in the image and their visual relationships are extracted using R‐CNN. The feature vectors, visual relationships, and their symbolic labels are stored in KD‐Tree data structures which can be used to predict the label of objects and visual relationships later. To facilitate semantic query, the images use the RDF data model and create an ontology for the symbolic labels annotated. For each query image, after extracting their feature vectors, the KD‐Tree is used to classify the objects and predict their relationship. After that, a SPARQL query is built to extract a set of similar images. The SPARQL query consists of triple statements describing the objects and their relationship which were previously predicted. The evaluation of the framework with the MS‐COCO dataset and Flickr showed that the precision achieved scores of 0.9218 and 0.9370, respectively. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases have surpassed cancer as the leading cause of death on the planet today. Numerous decision‐making systems with computer‐assisted support have been developed to assist cardiologists to detect heart disease, and thus, lowering the mortality rate. The purpose of this research is to classify audio signals received from the heart as normal or abnormal. The PhysioNet Computing in Cardiology (CinC) 2016 benchmark dataset, popularly known as PhysioNet 2016, has been used to validate the proposed methodology presented here. PhysioNet 2016 contains a total of 3200 phonocardiogram (PCG) recordings divided into sub‐datasets A‐F. The state‐of‐the‐art studies conducted till date have not considered the harmonic details of the beat that can be extracted from its equivalent chromagram image. In this work, textural features such as linear binary pattern (LBP), adaptive‐LBP, and ring‐LBP have been extracted from the existing spectrogram and combined with the features extracted from the chromagram. It has been observed that the combination of features extracted from both the image variants has resulted in a greater accuracy as compared to the scenario where researchers were using only the spectrogram. The experiment yielded the mean accuracy, precision, and F1‐score as 94.87, 93.11, and 95.273, respectively. The heart sound classification models employ spectrogram, scalogram, and mel‐spectrogram images to view and analyse the acoustic properties of a PCG signal. Although these visual tools provide useful information about the signal, yet they are unable to distinguish between pitch and resonance in heart sound generation. However, this paper proposes an alternative approach of heart sound signal representation that allows for a more precise measure of pitch‐related changes in the heart sound. Its results highlight the significance of extracting textural features from time‐chroma representation (i.e., chromagram) of PCG signals that have not been explored yet in the domain related to classification of heart sound signals as normal and abnormal. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding models are used to learn low‐dimensional representations of entities and relations in knowledge graphs. In this paper, we propose Multi‐RAttE, an attention‐based learning method for multiple relational knowledge graph embedding representation, which divides the information transfer in the knowledge graph into cross‐relational information transfer and relation‐specific information transfer, and divides the embedding of knowledge graph entities into structural embedding and multi‐relational embedding for joint learning. To objectively analyse the performance of the Multi‐RAttE model, we select two typical datasets and different representative baseline models for experimental evaluation on several tasks such as link prediction, multi‐relation prediction and node classification. The experimental results show that the Multi‐RAttE model improves 8% over the state‐of‐the‐art model Composition‐based Multi‐Relational Graph Convolutional Networks (CompGCN) in terms of Hits@1 metric on the link prediction task on FB15k‐237 dataset; on the multi‐relation prediction task, the accuracy improves by 1.8% and 3.7% in the auc metric and F1 metric, respectively. The experimental results have proved that the Multi‐RAttE model can effectively perform the representation of multiple relations. [ABSTRACT FROM AUTHOR]

Segmenting scientific abstracts into discourse categories like background, objective, method, result, and conclusion is useful in many downstream tasks like search, recommendation and summarization. This task of classifying each sentence in the abstract into one of a given set of discourse categories is called sequential sentence classification. Existing machine learning‐based approaches to this problem consider the content of only the abstract to obtain the neural representation of each sentence, which is then labelled with a discourse category. But this ignores the semantic information offered by the discourse labels themselves. In this paper, we propose LIHT, Label Informed Hierarchical Transformers – a method for sequential sentence classification that explicitly and hierarchically exploits the semantic information in the labels to learn label‐aware neural sentence representations. The hierarchical model helps to capture not only the fine‐grained interactions between the discourse labels and the words in the abstract at the sentence level but also the potential dependencies that may exist in the label sequence. Thus, LIHT generates label‐aware contextual sentence representations that are then labelled with a conditional random field. We evaluate LIHT on three publicly available datasets, namely, PUBMED‐RCT, NICTA‐PIBOSO and CSAbstract. The incremental gain in F1‐score in all the three cases over the respective state‐of‐the‐art approaches is around 1%. Though the gains are modest, LIHT establishes a new performance benchmark for this task and is a novel technique of independent interest. We also perform an ablation study to identify the contribution of each component of LIHT in the observed performance, and a case study to visualize the roles of the different components of our model. [ABSTRACT FROM AUTHOR]

In this paper, we have introduced some new similarity measures for q‐rung orthopair fuzzy sets and discussed their various features. These similarity measures have been constructed with the help of t‐conorms. The suggested similarity measures are more effective than the available ones in computing the similarity between distinct q‐rung orthopair fuzzy sets. With the help of these suggested similarity measures, we have also developed some novel entropy measures for q‐rung orthopair fuzzy sets. The newly proposed entropy measures are more effective than the existing ones in handling linguistic hedges. The applicability of the suggested similarity measures has been demonstrated in pattern analysis and the findings have been found to be better than due to existing ones. The traditional decision‐making method namely, the technique for order preference by similarity to the ideal solution gives us a compromise solution that is either closest to the positive ideal solution and farthest from the negative ideal solution but not both. So, to overcome this major drawback, we suggest a novel multi‐attribute decision‐making method in the q‐rung orthopair fuzzy environment and apply it to a decision‐making problem. [ABSTRACT FROM AUTHOR]

The increasing amount of electronic legal documents represents a great opportunity for the development of intelligent computational systems for legal texts processing and classification. Most of these systems use classical machine learning and large datasets in English. This paper proposes an approach to automatically predict legal case outcome from written description of the events in Arabic using deep learning. An in‐house corpus from the decisions of the Moroccan Court of Cassation is built and used to train a deep learning model that predicts judgement. As the created corpus is of limited size, a new data augmentation method is proposed to boost the prediction performance. Two settings for text representation are tested, namely FastText and GloVe embeddings, and multiple deep learning models architectures are tested. The proposed approach succeeds in predicting judicial decisions of the Moroccan Court of Cassation with an accuracy of 80.51% on six classes. Even with a small dataset, the proposed data augmentation method was helpful in improving the overall models' performance. Despite the advancement in the area of legal judgement prediction over the years, this work is the first attempt to predict legal outcome using the documents of the Moroccan Cassation court. The corpus created in the context of this work will be made publicly available to the community. [ABSTRACT FROM AUTHOR]

Countless cybercrime instances have shown the need for detecting and blocking obscene material from social media sites. Deep learning methods (DLMs) outperformed in recognizing obscene content flooded on many online platforms. However, these contemporary DLMs primarily treat the recognition of obscene content as a simple task of binary classification, rather than focusing on the labelling of obscene areas. Hence, many of these methods could not pay attention to the fact that misclassification samples are so diverse. Therefore, this paper focuses on two aspects (i) developing a deep learning model that could classify and label the obscene portion, and (ii) generating a labelled obscene image dataset with a wide variety of obscene samples to minimize the risks of inaccurate recognition. We have proposed a method named S3Pooling based bottleneck attention module (BAM) embedded MobileNetV2‐YOLOv3 (SBMYv3) for automatic detection of obscene content using an attention mechanism and a suitable pooling strategy. The key contributions of our article are: (i) generation of a well‐labelled obscene image dataset with a variety of augmentation strategies using Pix‐2‐Pix GAN (ii) modifications to the backend architecture of YOLOv3 using MobileNetV2 and BAM to ensure focused and accurate feature extraction, and (iii) selection of an optimal pooling strategy, that is, S3Pooling strategy, while taking the design of the feature extractor into account. The proposed SBMYv3 model outperformed other state‐of‐the‐art models with 99.26% testing accuracy, 99.39% recall, 99.13% precision, and 99.13% IoU values respectively. [ABSTRACT FROM AUTHOR]

One of the most important and costly stages in software development is maintenance. Understanding the structure of software will make it easier to maintain it more efficiently. Clustering software modules is thought to be an effective reverse engineering technique for deriving structural models of software from source code. In software module clustering, the most essential objectives are to minimize connections between produced clusters, maximize internal connections within created clusters, and maximize clustering quality. Finding the appropriate software system clustering model is considered an NP‐complete task. The previously proposed approaches' key limitations are their low success rate, low stability, and poor modularization quality. In this paper, for optimal clustering of software modules, Chaotic based heuristic method using a forest optimization algorithm is proposed. The impact of chaos theory on the performance of the other SFLA‐GA and PSO‐GA has also been investigated. The results show that using the logistic chaos approach improves the performance of these methods in the software‐module clustering problem. The performance of chaotic based FOA, SFLA‐GA and PSO‐GA is superior to the other heuristic methods in terms of modularization quality and stability of the results. [ABSTRACT FROM AUTHOR]

As an essential part of the intelligent transportation system, vehicular ad hoc network plays a vital role in the application of the Internet of Vehicles and security services. However, as the scale of the network continues to increase, trust‐based access and data sharing between vehicles have also become essential security issues. Given how to allocate vehicle resources safely, efficiently and reliably, this paper proposes a blockchain‐based key data sharing mechanism for the Internet of Vehicles. We first utilize certificateless public key cryptography to alleviate redundancy overhead and key escrow issues. Second, with the help of the security, decentralization, and non‐tampering characteristics of the blockchain, two smart contracts are designed and used to realize vehicle identity management and data access control. The identity chain smart contract solves the security problem of vehicle identity management, and the data chain smart contract ensures reliable transaction verification and efficient resource management. At the same time, an auction method is proposed to facilitate vehicle resource transactions. The experimental results show that our solution can realize secure data sharing of the Internet of Vehicles and ensure the operating efficiency of the system. Regarding system overhead, the proposed scheme has 30.6% and 33% lower private key generation overhead and 3.3% and 20.1% lower resource transaction time than AFSN and BSM under the same conditions. In addition, using our bidding method in data chain smart contracts can have a regulatory effect on market data transactions. In the aspect of resource transaction efficiency, for the change of the number of buyers, the transaction completion rate and transaction time of the proposed scheme are better than those of the contrast scheme. When the number of buyers is 100, the transaction completion rate was 9.2% higher than that of AFSN and 17.3% higher than that of BAM. The transaction time of the scheme is 32.2% and 48.1% lower than that of AFSN and BSM, respectively. [ABSTRACT FROM AUTHOR]

The prompt and accurate detection of brain tumours is essential for disease management and life‐saving. This paper introduces an efficient and robust completely automated system for classifying the three prominent types of brain tumour. The aim is to contribute for enhanced classification accuracy with minimum pre‐processing and less inference time. The power of deep networks is thoroughly investigated, with and without transfer learning. Fine‐tuned deep Residual Networks (ResNets) with depth up to 101 are introduced to manage the complex nature of brain images, and to capture their microstructural information. The proposed residual architectures with their in‐depth representations are evaluated and compared to other fine‐tuned networks (AlexNet, GoogLeNet and VGG16). A novel Convolutional Network (ConvNet) built and trained from scratch is also proposed for tumour type classification. Proven models are integrated by combining their decisions using majority voting to obtain the final classification accuracy. Results show that the residual architectures can be optimized efficiently, and a noticeable accuracy can be gained with them. Although ResNet models are deeper than VGG16, they show lower complexity. Results also indicate that building ensemble of models is a successful strategy to enhance the system performance. Each model in the ensemble learns specific patterns with certain filters. This stochastic nature boosts the classification accuracy. The accuracies obtained from ResNet18, ResNet101, and the proposed ConvNet are 98.91%, 97.39% and 95.43%, respectively. The accuracy based on decision fusion for the three networks is 99.57%, which is better than those of all state‐of‐the‐art techniques. The accuracy obtained with ResNet50 is 98.26%, and its fusion with ResNet18 and the designed network yields a 99.35% accuracy, which is also better than those of previous methods, meanwhile achieving minimum detection time requirements. Finally, visual representation of the learned features is provided to understand what the models have learned. [ABSTRACT FROM AUTHOR]

Human beings and other biological agents appear driven by curiosity to explore the affordances of their environments. Such exploration is its own reward – children have fun when playing – but it probably also serves the practical purpose of learning theories with which to predict outcomes of actions. Cognitive robots however have yet to match the performance of human beings at learning and reusing manipulation skills. In this paper, we implement a method that emulates the curiosity drive and uses it as a heuristic to guide (simulated) exploration of a particular task – pouring liquids. The result of this exploration is a collection of symbolic rules linking qualitative descriptions of object arrangements and the pouring action with qualitative descriptions of likely outcomes. The manner in which qualitative descriptions of object arrangements and actions are converted to numerical descriptions for the purpose of simulation parametrization is via probability distributions, which themselves are adjusted in the process of simulated exploration. This allows the grounding of the symbolic descriptions to attempt to adapt itself to the task. The resulting symbolic rules form a theory that, together with the probability distributions that ground it in numerical parametrizations, is intended to be used to predict qualitative outcomes or select manners of pouring towards achieving a goal. [ABSTRACT FROM AUTHOR]

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate a cube along a specified positional trajectory, but with no requirement for the cube to have any specific orientation. We used a relatively simple reward function, a combination of a goal‐based sparse reward and a distance reward, in conjunction with Hindsight Experience Replay (HER) to guide the learning of the DRL agent (Deep Deterministic Policy Gradient [DDPG]). Our approach allowed our agents to acquire dexterous robotic manipulation strategies in simulation. These strategies were then deployed on the real robot and outperformed all other competition submissions, including those using more traditional robotic control techniques, in the final evaluation stage of the RRC. Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory. The requirement to also orient the cube makes the agent less able to learn the task through blind exploration due to increased problem complexity. To circumvent this issue, we make novel use of a Knowledge Transfer (KT) technique that allows the strategies learned by the agent in the original task (which was agnostic to cube orientation) to be transferred to this task (where orientation matters). KT allowed the agent to learn and perform the extended task in the simulator, which improved the average positional deviation from 0.134 to 0.02 m, and average orientation deviation from 142° to 76° during evaluation. This KT concept shows good generalization properties and could be applied to any actor‐critic learning algorithm. [ABSTRACT FROM AUTHOR]

This paper describes the real‐time implementation of a sliding mode backstepping controller for boost converters for LED lighting applications. The sliding surface is used to calculate the first error value in the backstepping control procedure, which ensures the system's robustness over a wide range of disturbances by achieving an asymptotically stable system based on the Lyapunov function. Due to the non‐minimum phase nature of boost converter, the system will become unstable. Hence the output voltage is indirectly controlled by controlling the sliding surface in the proposed sliding mode backstepping controller in order to achieve the asymptotically stable system. The detailed simulation is carried out on the MATLAB/Simulink platform, and the results are compared to that of a conventional proportional‐integral‐derivative (PID) controller and a traditional sliding mode controller, which ensures system robustness across a wide range of load resistance and input voltage changes, resulting in enhanced transient and, steady‐state responses. In the laboratory, a prototype of proposed controller is designed to ensure its accuracy in real‐time. By comparing the proposed control technique to the conventional sliding mode controller and PID controller, the flexibility of the proposed control method is validated for any variation at the input and output sides. From the experimental results, it is apparent that the proposed controller responds accurately and rapidly regardless of the disturbances caused to the system. [ABSTRACT FROM AUTHOR]

Effective staff scheduling is a critical activity of successful software development management. Due to its difficulty and broad applications in many service delivery scenarios, staff scheduling has been studied for several decades. However, most existing work focus on constructing the working schedules based on a given workforce size. This paper tries to solve a prerequisite issue before performing staff scheduling, i.e., testing whether the already existed manpower can meet the scheduling requirements. Though it is possible to use network flow theory or artificial intelligence (AI) methods like genetic algorithms to solve this problem, their time complexities could be too high to be used for large problem sizes. This paper proposes a constructive method that can derive the minimum staff number for three scheduling problem variants in a linear running time, and in the meantime a corresponding working schedule that can satisfy all the problem constraints can be produced. We not only theoretically show the lower bound for the computation time complexity of our proposed method but also prove its correctness. Moreover, based on the derived minimum staff number, we further explore the genetic algorithm for generating the schedule and compare its performance with our method. The experiments show that our method outperforms the baselines in terms of both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This main intention of this paper is to adopt a new disease detection model for plant leaves. The proposed model involves several steps such as pre‐processing, leaf segmentation, abnormality segmentation, feature extraction and detection. Image scaling and contrast enhancement are performed during the pre‐processing phase. Once the pre‐processing is done, the segmentation phase starts with leaf segmentation by binary thresholding method and abnormality segmentation by K‐means clustering. Further, the local binary pattern and grey‐level co‐occurrence matrix features are extracted and a dimensionality reduced technique called principle component analysis is determined. As the main novelty, the weighted feature extraction is performed, in which the weight functions are optimized by the self‐adaptive deer hunting optimization (SA‐DHOA). Another contribution of this paper is to implement a hybrid classifier for disease detection. Here, the extracted weighted features are subjected to a support vector machine, and the abnormality segmented image is subjected to convolutional neural network (CNN), which is a deep learning algorithm that can learn the features automatically. Here, the same SA‐DHOA is used to improve the performance of CNN, which is termed as SA‐DHOA‐SCNN. Finally, the performance analysis confirms the maximum success rate of the proposed model over other conventional methods. [ABSTRACT FROM AUTHOR]

Although the country's general practitioner training system is constantly improving, problems such as large requirement gaps, low specialization, unfair resource allocation, and uneven regional distribution still exist. In order to promote the improvement of the general practitioner system, to solve the problems of low accuracy and poor robustness of the existing models for medical resource requirement prediction, this paper proposes a combination of grey‐scale prediction features and back propogation algorithm (BP) neural network medical resource requirement prediction algorithm. The algorithm first uses the principal component analysis algorithm to solve the principal components of the medical resource requirement influencing factors, then extracts the equal‐dimensional dynamic grey‐level optimization model grey prediction features of the principal component score, and finally inputs the features into the BP neural network to complete the medical resource requirement prediction. Subsequently, a large number of comparative experiments were carried out on the algorithm proposed in this paper using the medical resource requirement of a certain province as a data set. The experimental results have shown that the comprehensive improvement model proposed in this paper has the best effect in predicting the requirement of medical resources, which contains strong robustness and stability. The algorithm is suitable for the needs of medical resources at this stage. Predicting on the above has strong practical significance in many scenarios by using the proposed algorithm. [ABSTRACT FROM AUTHOR]

In order to explore scientific and effective mental health nursing intervention methods after gynecological tumour surgery, this paper combines the clustering algorithm to process the nursing method data. Aiming at the type of comprehensive design structure matrix, this paper compares and analyzes the pros and cons of current intelligent clustering algorithms and selects a high‐fit density algorithm as the clustering algorithm of the module division method. Moreover, this paper explains the relative meaning of module cohesion and coupling and uses the smallest ratio of cohesion and coupling as the criterion of the module division method to select the best results from the clustering. In addition, this paper combines the actual situation of mental health nursing intervention after gynecological tumour surgery to set the nursing intervention effect and analyzes the effectiveness of the centralized nursing intervention method through the contrast method. From the test results, it can be seen that the joint intervention nursing method has the best effect on the mental health nursing intervention after gynecological tumour surgery, which provides theoretical guidance for the subsequent mental health intervention after gynecological tumour surgery. [ABSTRACT FROM AUTHOR]

This article discusses the concept of data wisdom, which involves the critical thinking and judgment required to make data-based decisions. The special issue focuses on computing, intelligence, and data analytics, inviting high-quality research contributions that address real-world problems. One selected paper in the issue presents a new computer-aided diagnostic method for classifying different types of anemia using a hybrid approach. The authors hope that the research in this special issue will enhance readers' knowledge and inspire further work in intelligent systems and applications. The article expresses gratitude to the editor-in-chief, the editorial office staff, the authors, and the reviewers for their contributions. [Extracted from the article]

The mapping method that is based on the name and structure of the ontology elements is the strategy used in most mapping methods. Methods using the name often only use the similarity between the individual elements in the ontology to predict the semantic relations between two ontologies, while the latter measure the mapping between two ontologies by means of the structural relations between the elements. The effects of these two kinds of mapping strategies are not ideal. Addressing this issue, the work presented in this paper proposes an ontology mapping approach, in which the ontology element name and structure are combined. It uses the approaches based on linguistics and distance to generate a variable weight semantic graph. On this graph, the similarity of element names and structure are calculated through iterative computation. In the process of iteration, similarity result values are constantly adjusted. The approach avoids the problem of single methods that cannot use the entire amount of ontology information; therefore, it provides a more ideal mapping result. For making full use of the message of ontology, our implementation and experimental results are provided to demonstrate the effectiveness of the mapping approach. [ABSTRACT FROM AUTHOR]

Lack of open access, seizure specific database has hindered the development of Automated Seizure Detection System (ASDS) along with state‐of‐the‐art feature selection and classification methods. Available databases contain noise, artefacts, different length & time EEG segments, associated comorbidities, clinical settings, and epilepsy/seizure types etc. Pre‐processing of such continuous EEG segments requires significant amount of time and may feed redundant information (with reference to a seizure event) to the classification model leading to inaccurate, real‐time seizure event detection systems. Hence, this paper proposes a metadata generation of large EEG databases (here, CHB‐MIT EEG scalp database v.1.0.0) for ASDS. We elucidate the need to generate seizure sensitive data through peri‐ictal and non‐seizure EEG segments. This paper performs multi‐variate analysis and two class (non‐seizure and seizure event) classification between these fixed length and time EEG segments from support vector machine (SVM) and k‐NN classifier. We thoroughly analysed the variation and dependence of different kernels, cost function, gamma, and degree for SVM based pipeline. The proposed pipeline has been compared with state‐of‐the‐art pipelines and has achieved good classification score. Such methods will help in development of generalised approach toward handling large EEG databases and machine learning applications for seizure detection and prediction. [ABSTRACT FROM AUTHOR]

In order to improve the analysis effect of risk factors for blood transfusion in preterm infants, this paper studies the natural gradient algorithm based on statistical analysis methods and analyzes the data processing effect of the algorithm. Moreover, this paper combines the analysis of risk factors for blood transfusion in preterm infants to build a factor analysis system based on statistical analysis algorithms and intelligent algorithms, and combines back propagation (BP) neural networks to build the statistical intelligence model. In addition, this paper designs experiments to verify the performance of the statistical intelligence model constructed in this paper, combines statistical methods to perform data processing, and analyzes experimental results through control experiments. From the experimental research results, it can be known that the statistical processing method of risk factors proposed in this paper has a certain effect. Simultaneously, through a large amount of data training research and analysis, it is confirmed that the system constructed in this paper has good data processing effects and can be applied to practice. [ABSTRACT FROM AUTHOR]

With time smart services have become more domineering than ever before however, the pertinent security considerations fade to correspond with growing heterogeneity in the internet of things (IoT) devices and new technologies coupled with resource constraints, crafting IoT‐based systems more susceptible to cyber‐attacks. To ensure a secure IoT environment, pro‐active security mechanisms, like scanning vulnerabilities and prioritizing to remediate them timely, should be embedded in the system. Motivated by the facts, we in this paper, highlight the state of the art of several works trading with a common vulnerability scoring system (CVSS), its limitations, and the emendations recommended to conclude its maturity. CVSS is an industry standard that has been adopted worldwide to quantify the vulnerabilities in organizations for IT and IoT‐based systems. The vulnerabilities mathematical score coalesces with environmental knowledge for finding attack paths and apt score for prioritization. The specific functionality and exclusive dynamics of IoT and cyber‐physical systems in comparison to traditional computer networks, make the legacy cyber‐security exemplars unfit for these advanced networks. This paper studies the relevance of CVSS for smart systems and present an intelligent vulnerability quantification framework for IoT systems grounded on the CVSS v3.1 framework with threat intelligence and machine learning models. Further by applying blockchain technology in the proposed framework, the issues concerning security, lack of trust, and privacy possibly will resolve by hiring a smart contract. [ABSTRACT FROM AUTHOR]

The hike in the demand for smart cities has gathered the interest of researchers to work on environmental sound classification. Most researchers' goal is to reach the Bayesian optimal error in the field of audio classification. Nonetheless, it is very baffling to interpret meaning from a three‐dimensional audio and this is where different types of spectrograms become effective. Using benchmark spectral features such as mel frequency cepstral coefficients (MFCCs), chromagram, log‐mel spectrogram (LM), and so on audio can be converted into meaningful 2D spectrograms. In this paper, we propose a convolutional neural network (CNN) model, which is fabricated with additive angular margin loss (AAML), large margin cosine loss (LMCL) and a‐softmax loss. These loss functions proposed for face recognition, hold their value in the other fields of study if they are implemented in a systematic manner. The mentioned loss functions are more dominant than conventional softmax loss when it comes to classification task because of its capability to increase intra‐class compactness and inter‐class discrepancy. Thus, with MCAAM‐Net, MCAS‐Net and MCLCM‐Net models, a classification accuracy of 99.60%, 99.43% and 99.37% is achieved on UrbanSound8K dataset respectively without any augmentation. This paper also demonstrates the benefit of stacking features together and the above‐mentioned validation accuracies are achieved after stacking MFCCs and chromagram on the x‐axis. We also visualized the clusters formed by the embedded vectors of test data for further acknowledgement of our results, after passing it through different proposed models. Finally, we show that the MCAAM‐Net model achieved an accuracy of 99.60% on UrbanSound8K dataset, which outperforms the benchmark models like TSCNN‐DS, ADCNN‐5, ESResNet‐Attention, and so on that are introduced over the recent years. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing offers a digital infrastructure for smart city development. Cognitive cities are steadily automating daily urban processes. The ever expanding objective‐driven communities gather and share sensitive data that must be stored securely. Cloud computing offers a suitable platform that allows cognitive smart cities to access and re‐access data to learn from their past to adapt its current behaviour. However, the cloud is an untrusted entity that may expose data when decrypted for processing by systems. In this paper, we treat the issue of encrypted data processing. Often, the data is encrypted prior to transferring it to the cloud, where the cloud must have the data in clear to be able to make calculations which raises security and privacy threats if the cloud is considered untrusted. The scenario of asking users to make the calculations after decrypting the received cloud data and encrypting the obtained results before sending them back to the cloud is not a practical solution in distributed multi‐tenant architectures. Homomorphic encryption allows offers a solution for processing encrypted data. Many existing homomorphic encryption schemes suffer from limitations that hinder their usability. This paper presents an efficient fully homomorphic encryption scheme using twin key encryption and magic number fragmentation. The details of the scheme are presented along with cryptanalytic attacks to assess its effectiveness. The proposed scheme exhibits strong resilience against brute‐force attacks compared to its rivals from the literature. Finally, we illustrate the applicability of the proposed scheme using a cognitive smart city application. [ABSTRACT FROM AUTHOR]

Advance signal processing techniques are becoming one of the essential requirements for secure and efficient communication in the future innovative world. These requirements are due to the tremendous advancement of various latest cutting‐edge technologies that are very useful for the present generation. Marketing information system visualization is essential for the services of every advanced technology established in this way to reach people. Citing this, signal processing is a significant issue in most new technological innovations and challenges in various applications. A marketing information system, or MIS, based on these processes provides a valuable marketing method that helps marketers make better decisions. In this mode of operation, the existing methods are discarded, and the new methods, such as visual images, sound waves, and seismic waves, are implemented as digital signal processes, and their benefits are used as inputs for the processing of humankind. Based on this perspective, collaborative Management Information Systems (MIS) is proposed. This process‐based bridge enables MIS professionals to benefit from their investments in personnel, equipment, and business processes. In particular, transportation challenges such as total order intensity ratio, rising fuel prices, unexpected delays in transportation, shortage of skilled workers, deplorable warehouse conditions, insufficient and over‐stressed delivery staff, etc., and how road transport is giving its full attention to these fluctuations. Research paper explains very clearly and adequately explains the impact of such explained traffic problems, such as the volume of traffic and passengers, road network, lack of rehabilitation of half of the roads, and lack of national highways. Performance analysis for MIS measures with various factors is taken, and how the process varies in them and the answers to them are clarified through this research. This research citation shows how the marketing performance of an MIS measures the best practices in this research. Through this, online marketing performance based on MIS measures can be increased through MIS analysis reports for MIS measures. Finally, the Performance of the proposed MIS‐Transport with the MIS‐hospital Industry is taken as an example and explained. [ABSTRACT FROM AUTHOR]

Emotion recognition from facial image expression is always a problem for computer vision algorithms in classifying the human facial emotions. On other hand, over fitting is considered as a fundamental problem that prevents the deep learning model to fit the observed data on training data and unseen data on the test data. In this paper, we develop a convolutional neural network (CNN) based emotion recognition from facial images. Initially the study uses proper background removal tool to remove the image background and secondly, we use CNN to classify the facial features based mainly on the extraction of facial feature vector. The CNN is trained with huge number of facial features from the input images causes over fitting. This is mainly due to noise presence, limited training data size and classifier complexity and to avoid this, the study uses krill herd optimisation (KHO) that enables the fitting of the classifier. The simulation is conducted to test the efficacy of the model with expressional vector that recognizes the five types of facial features. The database consists of 20,000 images collected from 300 persons and the study obtains an accuracy of 98.6% during training and 98.45% during testing. The comparative analysis between various other deep learning models show that the proposed CNN‐KHO has higher rate of accuracy than other methods. [ABSTRACT FROM AUTHOR]

Recently, deep learning technology has gradually penetrated various fields. Today, the field of healthcare is also closely linked to deep learning technology. Image processing technology based on deep learning can accurately segment medical images, which is convenient for medical research and pathological analysis. Accurate distribution of images can effectively save medical resources. Therefore, image processing techniques can contribute to the quantification and assessment of economic audit risks. In recent years, medical image segmentation has achieved many research results. However, with the improvement of accuracy, the segmentation standards of medical images are also becoming more and more stringent. For medical images, they tend to have rough and fuzzy boundaries and noise disturbances of different shapes. The above problems pose challenges for accurate localization and segmentation of lesion regions. On the other hand, in the field of medical images, there are also problems such as unbalanced number of samples and scarcity of large medical image datasets. In response to these problems, this paper conducts research work and proposes an Attention Mechanism and Multi‐Scale spatial Pooling‐based conditional Adversarial Network (AM‐MSP‐cGAN) model to achieve automatic segmentation of medical images. AM‐MSP‐cGAN can learn more detailed features from fuzzy boundaries, and effectively solve the problem of data lack, thereby promoting economic audit risk quantification and assessment in the healthcare field. [ABSTRACT FROM AUTHOR]

With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual‐symptom relationship diagram with local differential privacy (LDP‐ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning‐based framework of LDP‐ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning‐based LDP‐ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP‐ISRD has good performance. [ABSTRACT FROM AUTHOR]

Virtualization enables the deployment of several virtual servers on the same physical layer, critical component of the cloud. As cloud services advance, more apps that use repositories are developed, which adds to the overburden. Containers have evolved into the most reliable and lightweight virtualization technology for cloud services thanks to their flexible sorting, mobility, and scalability. In container‐based clouds, containers can potentially cut data centre energy usage more than virtual machines (VMs) do. Containers are less energy intensive than VMs. Resource allocation is the most prevalent method in cloud systems. However, resource allocation in container‐based clouds (RAC) is innovative and complicated due to its two‐level architecture. This includes the pairing of virtual machines and physical computers with containers. In cloud container services, planner components are essential. This lowers expenses while improving the performance and variety of workloads using cloud resources. The cloud infrastructure resource allocation framework is gaining popularity since it is energy‐efficient and focuses on cloud data management to maximize income and minimize costs. In this paper, we proposed a deep learning‐based architecture capable of achieving high data centre energy efficiency and preventing Service Level Agreement (SLA) violations from deploying green cloud resources. This research describes a hybrid optimum and multi‐agent deep reinforcement learning (MADRL) technique for dynamic task scheduling (DTS) in a container cloud environment. The MADRL‐DTS model for the RAC problem considers VM overheads, VM types, and an affinity restriction. Then, to address the RAC issue, we develop a DTS hyper‐heuristic technique. MADRL‐RAC may give allocation rules by recognizing workload trends and VM types from previous workload traces. Compared to modern procedures, the results demonstrate a significant reduction in energy consumption. The evaluation for energy‐efficient resource allocation is tested in several virtualized environments to get a high power usage effectiveness and CPU usage. [ABSTRACT FROM AUTHOR]

Via Granular Computing (GrC), one can create effective computational frameworks for obtaining information from data, motivated by the human perception of combining similar objects. Combining knowledge gained via GrC with a Fuzzy inference engine (Neural‐Fuzzy) enable us to develop a transparent system. While weighting variables based on their importance during the iterative data granulation process has been proposed before (W‐GrC), there is no work in the literature to demonstrate effectiveness and impact on Type‐2 Fuzzy Logic systems (T2‐FLS). The main contribution of this paper is to extend W‐GrC, for the first time, to both Type‐1 and Type‐2 models known as Radial Basis Function Neural Network (RBFNN) and General Type‐2 Radial Basis Function Neural Network (GT2‐RBFNN). The proposed framework is validated using popular datasets: Iris, Wine, Breast Cancer, Heart and Cardiotocography. Results show that with the appropriate selection of feature weight parameter, the new computational framework achieves better classification accuracy outcomes. In addition, we also introduce in this research work an investigation on the modelling structure's interpretability (via Nauck's index) where it is shown that a good balance of interpretability and accuracy can be maintained. [ABSTRACT FROM AUTHOR]

In medical science, imaging is the most effective diagnostic and therapeutic tool. Almost all modalities have transitioned to direct digital capture devices, which have emerged as a major future healthcare option. Three diseases such as Alzheimer's (AD), Haemorrhage (HD), and COVID‐19 have been used in this manuscript for binary classification purposes. Three datasets (AD, HD, and COVID‐19) were used in this research out of which the first two, that is, AD and HD belong to brain Magnetic Resonance Imaging (MRI) and the last one, that is, COVID‐19 belongs to Chest X‐Ray (CXR) All of the diseases listed above cannot be eliminated, but they can be slowed down with early detection and effective medical treatment. This paper proposes an intelligent method for classifying brain (MRI) and CXR images into normal and abnormal classes for the early detection of AD, HD, and COVID‐19 based on an ensemble deep neural network (DNN). In the proposed method, the convolutional neural network (CNN) is used for automatic feature extraction from images and long‐short term memory (LSTM) is used for final classification. Moreover, the Hill‐Climbing Algorithm (HCA) is implemented for finding the best possible value for hyper parameters of CNN and LSTM, such as the filter size of CNN and the number of units of LSTM while fixing the other parameters. The data‐set is pre‐processed (resized, cropped, and noise removed) before feeding the train images to the proposed models for accurate and fast learning. Forty‐five MR images of AD, Sixty MR images of HD, and 600 CXR images of COVID‐19 were used for testing the proposed model 'CNN‐LSTM‐HCA'. The performance of the proposed model is evaluated using six types of statistical assessment metrics such as; Accuracy, Sensitivity, Specificity, F‐measure, ROC, and AUC. The proposed model compared with the other three types of hybrid models such as CNN‐LSTM‐PSO, CNN‐LSTM‐Jaya, and CNN‐LSTM‐GWO and also with state‐of‐art techniques. The overall accuracy of the proposed model received was 98.87%, 85.75%, and 99.1% for COVID‐19, Haemorrhage, and Alzheimer's data sets, respectively. [ABSTRACT FROM AUTHOR]

If not well identified and controlled, risks in systematically engineered cold supply chains can lead directly to food safety incidents. In current approaches to cold supply chain risk evaluation, there is a lack of systematic classification and quantitative analysis of the influences of risk factors in the chain. To accurately evaluate unknown risks that can exert a fluctuating influence and result in great losses, this study builds a knowledge base of expert systems based on expertise and extensive experience and modifies the expert weights in conjunction with entropy weights to reduce subjective error. An ordered weighted average (OWA) operator is used to evaluate risk in a cold supply chain effectively. First, in combination with a case study of a typical fresh food e‐commerce enterprise, a typical fresh food e‐commerce enterprise, this paper identifies the risks in a food refrigeration supply chain. Second, relying on an expert group with professional knowledge and rich experience, an expert system knowledge base is constructed, risks are assigned, and the expert weight is modified in combination with the entropy weight method. Finally, based on the OWA operator, the modified risk assignment is effectively evaluated, and the risk value is obtained and sorted. Specific risk control measures are put forward according to the results of the calculation. The results show that an evaluation method combining an expert system knowledge base and the entropy‐OWA method can effectively depict and process the evaluation information needed for risk rankings and solve the problems of supply chain risks more rapidly and effectively. [ABSTRACT FROM AUTHOR]

The world is affected by COVID‐19, an infectious disease caused by the SARS‐CoV‐2 virus. Tests are necessary for everyone as the number of COVID‐19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID‐19 in a few seconds by uploading a single chest X‐ray image. A deep learning‐aided architecture that can handle client and server sides efficiently has been proposed in this work. The front‐end part has been developed using StreamLit, and the back‐end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID‐19 pandemic and will help to push the envelope of this work to a different extent. [ABSTRACT FROM AUTHOR]

Interpreting high‐resolution satellite imagery could be an expensive and time‐consuming task for human eyes. Computer Vision and Deep Learning techniques can help to solve this major problem by applying detection algorithms, which can ease the task of analysing such images for the benefit of humans. It can help in changing the way we comprehend and anticipate the economic activity around the world. Such techniques help us to observe the urban development in high security areas such as national and international borders. Constant progressions in improving and making satellites deployment, a cost‐effective process to strengthen the networks of satellite orbiting the earth is one of the reasons such tasks can be easily solved with the help of high‐resolution images. Current computer vision research works have achieved significant milestones in accuracy and speed but, there are still room for improvements. In this paper, we addressed some of these methods to bring them to a combined pipeline and proposed a set of improvements to further improve the speed and the accuracy of the detections. We proposed a unified framework, which combines several object detection algorithms and the state‐of‐art architecture of YoloV4 along with the TensorFlow object detection API. This framework can detect small and well as large objects with improved speed and accuracy by using two detectors for different scales. Evaluation ran on these high‐resolution images yield mAP of 85.6% F1‐score of 0.84. [ABSTRACT FROM AUTHOR]

Upright balance control is a fundamental skill of bipedal robots for various tasks that are usually performed by human beings. Conventional robotic control is often realized by developing accurate dynamic models using a series of fixed torque‐ankle states, but their success is subject to accurate physical and kinematic models. This can be particularly challenging when external disturbing forces present, but this is common in unstructured robotic working environments, leading to ineffective robotic control. To address such limitation, this paper presents an adaptive ankle impedance control method with the support of the advances of adaptive fuzzy inference systems, by which the desired ankle torques are generated in real time to adaptively meet the dynamic control requirement. In particular, the control method is initialised with specific external disturbing forces first representing a general situation, which then evolves whilst performing in a real‐world working environment by acting on the feedback from the control system. This is implemented by initialising a rule base for a typical situation, and then allowing the rule base to evolve to specific robotic working environments. This closed loop feedback and action mechanism timely and effectively configures the control system to meet the dynamic control requirements. The proposed control method was applied to a bipedal robot on a moving vehicle for system validation and evaluation, with robotic loads ranging from 0 to 1.65 kg and external disturbances in terms of vehicle acceleration ranging from 0.5 to 1.5 m/s2, leading to robotic swing angles up to 7.6º and anti‐disturbance timespans up to 8.5 s. These experimental results demonstrate the power of the proposed upright balance control method in improving the robustness, and thus applicability, of bipedal robots. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Machine learning tools for analysing lubricating oil data have enabled better information in the condition based maintenance (CBM) approach to the process of diagnosing and predicting failures in diesel‐powered vehicle fleets. With the increase in the number of sensors inserted in vehicles, it is possible for companies to stockpile large quantities of information in real time. As such, the development of data‐driven tools will enable accurate identification of the level of wear of a system, evolving CBM into a more reliable and dynamic approach. Following this type of data‐based analysis focusing on determining the wear of systems and equipment, this paper presents an intelligent system for assessing the condition of lubricating oil in automotive diesel engines. To this end, we analyse the use of raw data obtained from the sensors installed in the car and evaluate in conjunction with the insertion of engineered features designed the best way to determine the operating state of the oils. The results presented in this analysis show that to explain 90% of the variation in the original data only the variables kinematic viscosity, dynamic viscosity, engine oil temperature and OSF_v3 are needed. After evaluating the quality of the variables, we conducted an experimental study to analyse the performance of various machine learning algorithms, taking into account the number of features as input data. The results show that the proposed system has the ability to identify the operating conditions of lubricating oil using seven variables as input to a model based on gradient boosting, obtaining a recall result of 93%, precision of 96% and F1‐score of 94%. We conducted a set of additional studies to understand how different subsets of variables affected the performance of the models, and the results show that the best combination includes information regarding the engine speed, coolant and oil temperature, oil pressure, the oil stress factor (OSF_v3), kinematic viscosity, and the dynamic viscosity. [ABSTRACT FROM AUTHOR]

Today, Internet of things (IoT), Cloud and Fog networks have spread out around the world. The more these networks grow, the more their energy consumption comes to attention. Many efforts have been made during recent years to decrease this energy consumption, mainly focused on utilizing low‐power devices. Green algorithms are recently proposed to reduce energy consumption by modifying the structure of many algorithms employed in the network and its protocols. This paper proposes a new green reliability algorithm for Transmission Control Protocol/Internet Protocol (TCP/IP protocol) in Fog computing. The proposed algorithm does not require extensive TCP/IP protocol changes or relevant hardware. It is based on transferring less number of packets in the network by using the advantage of differences between TCP and User Datagram Protocol (UDP). TCP and User Datagram Protocol (UDP) are different in nature as the number of total packets in UDP is half that of TCP. As a result, the number of complete packets in UDP is half that of TCP. The proposed method is built around the loss of some packets in applications, such as voice and online video, does not severely degrade the end results. Therefore, the UDP protocol can substitute TCP in such situations. The criterion to switch between the two is the minimum acceptable Quality of Service (QoS) of the overall network. In other words, the UDP protocol will be used as long as QoS requirements are met. The switching process between UDP and TCP is dynamic, optimized by estimating network noise in the period. Additionally, we evaluated the proposed method based on several QoS functions, including delay, throughput, and energy usage. [ABSTRACT FROM AUTHOR]

The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment analysis. A survey of the work that has been done till now is conducted observing the results and outcomes concerning varying parameters of various researchers who worked on previously existing as well as novel and hybrid algorithms opting legion methodologies. The fundamental algorithms like Support Vector Machine (SVM), Bayesian Networks (BN), Maximum Entropy (MaxEnt), Conditional Random Fields (CRF) and Artificial Neural Networks (ANN) are also discussed to achieve practice percentage and accuracy score in the field of NLP, sentiment analysis and text analytics. Various other novel approaches and algorithms like CNN, LSTM, KNN, K*, K‐means, K‐means++, SOM and ENORA, along with their limitations and the performance metrics providing accuracies for major open data sets are also analyzed. [ABSTRACT FROM AUTHOR]

Machine learning are vulnerable to the threats. The Intruders can utilize the malicious nature of the nodes to attack the training dataset to worsen the process and manipulate the learning and make the over all system with less efficiency and performance. The optimized poison attack procedures are already introduced to estimate the overall bad scenario, design the intrusion as bi‐level optimization and it is considered computational complexity is high and demanding, in contrary the applicability is limited such models deep neural networks. In this research papers, we have proposed, novel proposed system, poisoning attacks against the Machine learning training dataset, including the genuine data points that reduce the accuracy of the classifier in the process of training. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks. [ABSTRACT FROM AUTHOR]

Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatio‐temporal data from the real‐time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio‐temporal interaction data, focusing on two areas: exceptional subgroups and spatio‐temporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data. [ABSTRACT FROM AUTHOR]

Watermark adding is one of the important means for image security and privacy protection in Internet of things (IOT) applications based on federated learning. It is often inseparable from adversarial training with watermark removal algorithms. The effect of watermark removal algorithms will directly affect the final result of watermark addition. However, the existing watermark removal algorithms have drawbacks such as incomplete image watermark removal, poor image quality after watermark removal, large demand for training data, and incorrect filling, which seriously affects the development of image information security and privacy protection in IOT applications based on federated learning. To solve the above problems, this paper proposes an improved image watermark removal convolutional network model based on deep image prior. First, we improve the U‐Net network model, using six downsamping layers and six deconvolution layers combined with deep image prior method to reduce the loss of details and perceive high‐level features, thereby improving the ability of the network to extract high‐level features of the image. In addition, we design a new type of loss function which is called stair loss, and add L1 loss and perception loss to establish new constraints. In order to verify the effectiveness of our method, a comprehensive experimental comparison was conducted on the public dataset PASCAL VOC 2012 in the same experimental environment with CGAN and the deep prior method. The experimental results show that the improved model combined with the deep image prior method can extract the high‐level feature information and can directly remove the watermark from the picture without pretraining the network, the L1 loss and perceptual loss can better retain the image structure information and speed up the watermark removal of the model, the stair loss corrects the final output more accurately by correcting the output of each layer; our method improves the learning ability of the model, and under the condition of the same training time, the image quality after watermark removal is higher, and the final watermark removal result is better, which is more suitable for distributed structure of IoT application based on federated learning. [ABSTRACT FROM AUTHOR]

6G networks are envisioned to provide an extremely high quality‐of‐service (QoS). Then, future 6G network must operate in the Ka‐band, where more bandwidth and radio channels are available, and noise and interferences are lower. But even in this context, 6G base stations must adjust the transmission power to ensure the signal‐to‐noise ratio is good enough to enable the expected QoS. However, 6G networks are not the only infrastructure operating in that band. Actually, many scientific instruments are also working on those frequencies. Considering that 6G networks will be transmitting a relevant power level, they can interfere very easily with these scientific instruments. Therefore, in this paper we propose a new solution to enable the interferenceless coexistence between 6G networks and scientific instruments. This solution includes a three‐dimensional model to analyse future positions of user devices. Using this information and an interference model, we design a decision model to adapt the transmitted power, so the QoS achieves the expected level. Besides, when the transmitted power is high enough to interfere with close scientific instruments, a scheduling algorithm based on swarm intelligence is triggered. This algorithm calculates the optimum distribution of time slots and radio channels, so the scientific instruments can operate, and the 6G networks can still provide the required QoS. An experimental validation is provided to analyse the performance of the proposed solution. Results show a complete coexistence may be achieved with an interference level of −26 dBm and a QoS above 95% of the expected level. [ABSTRACT FROM AUTHOR]

Objective Method Results Discussion Conclusion The digital sound recording of several heart sounds (HSs) is named phonocardiogram (PCG) signal. Analysing these PCG signals is essential for diagnosing diverse sorts of heart disorders. Nevertheless, owing to troubling surrounding noise signals, PCG signal recording is challenging. So, before utilising PCG for advanced processing, de‐noising the PCG signal is executed. A new sliding window adaptive noise cancellers (SWANC)‐centred filter technique is proposed in this paper for de‐noising along with recovering the PCG signal effectively.Utilising the least mean square (LMS), an SW optimum adaptive filter (AF) structure is introduced in this work for estimating a De‐noised signal (DS) with better accuracy. Here, via the SWAF stage, a noisy signal is processed. An SW of fixed duration slides over the signal; also, the signal is filtered utilising the AF in each window. Utilising this SWAF architecture, this method approximates the PCG signal's clean version with better accuracy.The proposed robust SWAF is analogized to experimental PCG signals that were corrupted by Gaussian noise (GN) together with pink noise (PN) with distinct noise levels (NLs). From the physionet database, the experiential data are acquired. The outcomes exhibited that a remarkable performance was attained by the robust SWAF model.A 2–10 times decrease in MSE values was attained by the proposed filter structure when analogized with conventional LMS filter configuration. Further, the SNR is improved by 3 times and comparatively, the PSNR enhancement was 4%–25%. The association betwixt the clean signal along with its estimate is greater than 0.92.A cost‐efficient hardware installation of ANC with higher accuracy was offered by the SW LMS AF model. For obtaining desired convergence speed and accuracy, such models are tested for real‐time performances in the future. [ABSTRACT FROM AUTHOR]