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The next paper is titled "Iterative learning fuzzy control for nonlinear systems with adaptive gain and without resetting condition", and authored by Farah Bouakrif, Tarek Bensidhoum, and Michel Zasadzinski. Authors of this paper present a simple online tuning procedure for target control infusion (TCI) in the control of the neuromuscular blockade (NMB) level by administration of the muscular relaxant rocuronium. This issue of Interdisciplinary Methods and Approaches for Cybernetics and Systems Modeling includes a collection of extended versions of best presented papers in CoDIT 2020 conference. [Extracted from the article]

The next paper is titled I "Adding Interpretability to Neural Knowledge DNA". i It addresses in a novel way one of the biggest challenges of Artificial Intelligence - explainability. It is an alternative conceptualization of artificial intelligence (AI) that focuses on AI's assistive role, emphasizing the fact that cognitive technology is designed to enhance human intelligence rather than simply replacing it. The Authors introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA explainable, which is a big step toward trust in Artificial Intelligence (AI). [Extracted from the article]

Presents observations on the conference papers on cultural systems and organizational processes in the U.S. Basis for descriptive precision; Identification of the most basic empirical questions regarding culture; Distinct formalization of social systems.

In the following paper titled " I Evaluating Industry 4.0 Implementation Challenges Using Interpretive Structural Modelling and Fuzzy-Analytic Hierarchy Process i " the authors contribute to the Industry 4.0 related works by identifying, modeling, analyzing and prioritizing the challenges in implementing Industry 4.0 in manufacturing industries. The paper introduces a smart support system for selecting a group of reviewers to evaluate a particular problem, such as a research proposal or a research paper. The aim of this Guest Edition of I Cybernetics and Systems i is to present a wide-ranging scale of concepts and processes being currently researched, developed, and evaluated in real life settings in anticipation of incoming Industry 4.0 era. [Extracted from the article]

The author reflects on the life of professor Marco Somalvico. He sees Somalvico as one of the first two scholars to introduce research on artificial intelligence (AI) in the Italian acamedia who also undertook research on robotics. An overview of Somalvico's early to middle life is presented. The author noted Somalvico as humane and noble human being.

Errors in Cyber-Physical Systems present a major problem given the current state of technological complexity. Self-diagnosis can contribute to address it, being the standpoint of the present paper. Hence, an application of exploratory Machine Learning models to assess the functioning of robot software in order to identify anomalies that lead to low performance is proposed. More precisely, Hybrid Unsupervised Exploratory Plots (HUEPs) are extended through density-based clustering techniques, that are applied together with unsupervised exploratory projection models. As a result, intuitive and informative visualizations of software performance are obtained, supporting the monitoring and anomaly detection tasks. The proposed clustering extension of HUEPs is thoroughly validated on a massive and up-to-date open dataset, obtaining promising results. [ABSTRACT FROM AUTHOR]

Reviewing is the most important step in the quality accreditation of scientific works, requires the professional expertise of the reviewer as well as there is no conflict of interest in the evaluation process. However, we also acknowledge that reviewers have limited knowledge, experience and opinions about the work of others, so they may misinterpret the author's point of view, leading to the rejection of excellent scientific work or a potentially successful project proposal. Manually selecting reviewers can lead to bias and time-consuming. To solve these problems, we have developed a support system for selecting a group of reviewers to evaluate a particular problem, such as a proposal or a research paper. Our support system consists of three main modules: data collection, reviewer identification and group prediction of reviewers. The Data Collection module collects data from a variety of sources to create the scientist profile database. The reviewer identification module recognizes reviewers on a specific topic. The reviewer prediction module provides a group of exp reviewers to evaluate a submitted paper or a proposal. Experiments on the DBLP computer science bibliography dataset showed that our system achieves better results in terms of accuracy in comparison to other methods. [ABSTRACT FROM AUTHOR]

The paper defines the concept of an information graph and proposes its use to describe information links and analyze the information flow in any organization regarding information security. Research on information graphs in this aspect should be developed, especially in identifying "dangerous graph structures" such as the Hamiltonian graphs or cliques. Information graphs containing such a structure as a Hamilton cycle can be analyzed using results from the theory of finding and existence of Hamilton cycles, an essential branch of graph theory. The paper also describes a graph of potential security conflicts, a thematic graph, i.e. a specific information graph. Two theorems concerning the properties of information graphs are also proved. [ABSTRACT FROM AUTHOR]

In recent years, with tremendous progresses of deep learning in multiple disciplines, there are several advanced sequential neural-network (NN) based architectures (e.g., recurrent neural network—RNN, Auto-Encoding—AE, transformer, etc.) have been proposed. Recently, there are several well-known GNN-based architectures like as graph convolutional network (GNN) have been proposed to deal with challenges related to the global representation preservation of text. However, most of recent proposed GNN-based text-embedding models still be unable to integrate the global structure with the semantic sequential representations of words/sentences into the unified textual embedding space. Moreover, they are also considered as unable to learn the rich context-varied representations of words. In order to tackle aforementioned challenges, in this paper we proposed a novel integrated text graph representation learning approach, named as: GOWSeqGCN. Our proposed GOWSeqGCN is an integrated semantic graph-of-words sequential textual representation under the graph convolutional network framework. In order to demonstrate for the effectiveness of our proposed GOWSeqGCN model in comparing with recent state-of-the-art text representation learning baselines, we conducted extensive experiments in benchmark textual datasets. The experimental outputs showed the outperformances and necessary of our proposed ideas in this paper. [ABSTRACT FROM AUTHOR]

This paper offers insights about the management of policy issues such as COVID 19, climate change, sustainability, terrorism and many more emerging from an increased globalization of local problems in society. It discusses this management with the support of the Viable System Model -VSM-, assuming that the reader is already familiar with Beer's work and also with Espejo's work. Its focus is an extension of the VSM -the Enterprise Complexity Model- to improve relationships in organizational system. It discusses international relations making reference to the Westphalian Treaty of 1648, but only as an origin of fragmentation in public structures. At a most fundamental level it is about the management of complexity in organizational systems through the application of recursive structures that the paper develops in some depth. [ABSTRACT FROM AUTHOR]

Presents an overview of the panel papers on cybernetics and systems research presented at a session on Cultural Systems in Europe. Mathematical approaches applied to central problems addressed by cultural anthropologists; Awards and achievements of the panel papers; Discussion on mathematical methodology in the published work.

Knowledge Management (KM) research outputs have been expanding exponentially in the past years, generating diversified topics, which lack integration and classification. It has been challenging for experts to classify KM because of its versatile open fields, and in our view, it contributes to the technocratic approach remaining behind the organizational approach. This paper highlights a way to classify KM publications through a pattern that will support technocratic developments representing knowledge in a more explicit form. This study uses a classification method that uses a template in a taxonomy shape, executing some procedures and allowing an accurate identification and organization of KM research outputs. The proposed taxonomy method is proven on a set of 150 different KM publications from the last 15 years. This scheme is grouped into two main categories: Conceptual and Empirical which could enable academics and practitioners alike to better understand the current gaps that are prevalent in KM. [ABSTRACT FROM AUTHOR]

Recently, Collective Intelligence has been widely applied to solve a wide range of complex problems. The underlying mechanism behind this notion is a group of individuals as a whole can display abilities not shown by individuals. In addition, the main aim of Collective Intelligence is to maximize groups' potential. Many studies have investigated analyzing the roles of collective characteristics in fostering collective intelligence. Independence is an important principle of collective intelligence beside diversity, decentralization, and aggregation. By independence, one can have in mind the phenomenon in which collective members are freely in providing individual solutions to a given problem. This paper presents an analysis of the impact of independence on collective performance. For such a task, we propose an independence measure by taking into account individual reputations and graph theory. Then computational experiments have been conducted to determine the impact of independence on collective performance. The findings have revealed the positive role of independence in fostering collective intelligence. [ABSTRACT FROM AUTHOR]

An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people's diverse handwriting styles. Therefore, this paper aims to develop Kannada handwritten script recognition framework by ensemble methods. Initially, the essential Kannada handwritten text images are collected. These collected images are used for pre-processing with the CLAHE and filtering techniques and followed by segmentation with the active contour approach. The segmented images are utilized using the Adaptive Local Tetra Pattern with freeman chain code histogram techniques. Here, the parameters in the LTrP are tuned with Hybrid Honey Badger Henry Gas Solubility Optimization. Finally, the handwritten recognition is performed by Hybrid Feature Extraction with Ensemble Deep Learning (HFE-EDH). Further, the recognition rate is elevated with the parameter optimization in deep learning approaches using the HGSO + HBA algorithm. Throughout the result analysis, the accuracy of the designed method attains 96%. Thus, the proposed method reveals better performance regarding various performance measures. [ABSTRACT FROM AUTHOR]

The blockchain model exposed its adaptability in various areas, including inter-banking, supply chain management, international payment, etc. The anomaly intrusion in blockchain mostly threatens the privacy and security of information, thus secure intrusion detection technique is highly essential. Presently, blockchain is incorporated into intrusion detection model for improving the overall system performance. However, the existing methods are not sufficient for detecting recent network attacks. Therefore, the blockchain-enabled intrusion detection is developed in this paper based on the Battle Royale Dingo optimization (BRDO) driven deep stacked network model. Here, a deep stacked network is applied for detecting the intrusion, and it is trained based on the optimization model for enhancing detection performance. The designed BRDO-based deep stacked network achieved improved performance than traditional techniques with regards to testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) of 0.9106, 0.9180, and 0.9186. [ABSTRACT FROM AUTHOR]

For securing the network, intrusion detection systems are frequently used in wireless sensor networks to fight against insider attacks by adopting the appropriate trust-based methods. Still, the sensors could create an enormous amount of data that reduces the efficacy of trust computation in the big data era. This paper aims to introduce a new attack detection system under the big data perspective. Originally, the input data is fed the preprocessing phase, in which the normalization process takes place. Further, the MapReduce framework is used to handle the bulk data by reducing it. The preprocessed data is subjected to extract the features, where it extracts the raw features, statistical features, and higher-order statistical features. Here, the feature selection is done by chi-square ranking and info-gain ranking. Further, these features are provided as the input to the classification phase, where multilayer perception (MLP) is used for detecting the presence of an attack. For making the classification more precise, weights of MLP are optimally tuned by proposed slap swarm updated sparrow optimized algorithm which integrated the concept of sparrow search algorithm and salp swarm algorithm. Finally, the performance of presented scheme is calculated to existing approaches under different metrics. [ABSTRACT FROM AUTHOR]

Task scheduling is the act of allocating tasks in a certain way to make the best use of the resources at hand. Users of the service must make their demands online since cloud computing is the method used to offer services through the internet. In this paper, a new hybrid optimization model is introduced for secure task scheduling in cloud which includes six fold objective functions such as makespan, execution time, Quality of Service (QoS), utilization cost and security. In security constraint, trust evaluation and risk probability was determined. Black Widow Combined Seagull Optimization (BWCSO) algorithm was proposed for obtaining the best optimization result by combining Black Widow Optimization (BWO) and Seagull Optimization Algorithm (SOA). Cycle crossover (CX) was introduced to produce an offspring from its parents in which each slot is filled by an element from a different parent. Finally, the suggested algorithm's performance was assessed, and the best outcome was found with respect to makespan. [ABSTRACT FROM AUTHOR]

This paper plans to implement the benefit of deep hybrid learning for fingerprint liveness detection. The fingerprint image is subjected to background removal using Active contour. Further, the feature extraction is performed by the optimized Scale-Invariant Feature Transform (SIFT) and hybrid feature descriptor. New Modified Shark Smell Optimization (M-SSO) is used for developing optimized SIFT keypoints. The hybridization of the Local Directional Pattern (LDP) and Local Binary Pattern (LBP) is used as the feature pattern extraction. The classification phase combines Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The optimized SIFT keypoints are taken as input by RNN, and the patterns resulting from hybrid LDP and LBP are taken as input by CNN. The M-SSO optimizes the number of hidden neurons of both RNN and CNN to maximize classification accuracy. The proposed approach significantly improves the performance of FLD on different benchmark datasets over state-of-the-art models. [ABSTRACT FROM AUTHOR]

In this paper, the issue of extended dissipative control for interval type-2 fuzzy Markov jump systems subject to actuator faults is investigated. It is different from the traditional T-S fuzzy method, the interval type-2 fuzzy set is introduced to model the system, and the nonlinearity with parameter uncertainty is captured using the interval type-2 Takagi-Sugeno fuzzy model. During the implementation of the control process, the possible actuator failures are fully considered when designing a controller, and a fault-tolerant control strategy is employed to enhance the elasticity of the designed controller. It is worth noting that, in order to better adapt to the characteristics of interval type-2 fuzzy system, the designed controller follows non-parallel distribution compensation rules, that is, the designed controller does not have the same premise structure and fuzzy rules as the system model, which further improves the flexibility of controller design. Some sufficient conditions ensuring the stability with the extended dissipative performance of the systems are established. In the meanwhile, the obtained results with less conservatism are ensured owing to the boundary information of membership functions considered in the stability analysis. Finally, two numerical examples are provided to indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) devices, IoT applications require real-time and low-latency responses. Fog computing is a suitable platform for processing Internet of Things applications. However, fog computing devices are distributed, dynamic, and have limited resources, so the allocation of fog computing resources to execute heterogeneous and delay-sensitive tasks in the Internet of Things is a significant challenge. In this paper, we mathematically formulate the resource allocation problem to minimize the makespan while meeting the quality of service (QoS) requirements of IoT tasks. Next, we propose two learning automata, namely an automaton for select tasks (ATF) and an automaton for select virtual machine (Avf) to efficiently map IoT tasks to FNs. In this approach, a task is selected from the set of ATF actions and then, a Fog node is selected from the set of Avf actions. If the requirements for executing the tasks on the fog nodes are met, then the resource is allocated to the task. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different fog configurations. The proposed algorithms are evaluated in a simulation environment by extending the iFogsim to simulate a realistic fog environment. The experimental results indicate that the performance of the proposed algorithm is better compared with existing algorithms in terms of MK, response time, delay, Processing time, and cost with the increasing number of task submissions. [ABSTRACT FROM AUTHOR]

Anomaly detection and state prediction research using multivariate data is being actively conducted in various industrial fields. However, since most dynamically operating industrial machines perform different operating conditions, they contain different types of abnormal conditions, making it difficult to detect anomalies and predict the remaining life. This white paper proposes a condition diagnosis model based on multi-sensor data prediction for estimating the remaining lifespan of equipment while solving two complex problems of detecting four typical abnormal conditions and sensor omissions of industrial machines. First, we use a multi-sensor data generation model to learn relationships between sensors, and second, we use a sensor data prediction model to learn sensor-specific feature information. In order to extract the temporal and spatial characteristics of sensing information and to derive the relationship between the sensors, we propose an attention model with three types of cases. Finally, the state of the device is diagnosed through the difference between the model predicted value and the actual value, and future state information of the device is predicted through the accumulation of error information. In order to prove the robustness of the proposed model, extensive experiments were conducted focusing on the case where sensor omission occurred due to data from equipment with more than 4 types and conditions. Our model produces missing sensor data with about 92% accuracy and detects anomalies with about 88% accuracy, even if parts of the sensor are missing or the operating environments have been changed. The proposed model has improved anomaly detection accuracy compared to the comparative model, and has been proven to be applicable to real industrial problems. [ABSTRACT FROM AUTHOR]

Many industrial companies capture high volume of time series data from their industrial processes. However, to visualize it, regular visualization approaches require specialized hardware. Thus, downsampling algorithms are required to create a simplified view of the original data. Although industrial processes involve synchronized variables that should be visualized together for their analysis, existing downsampling algorithms tackle visualization of univariate data series. This paper proposes an adaptive extension of the M4 algorithm for multivariate datasets. The algorithm has been validated successfully with data from a conventional 3D turning operation and commodity hardware. For validation, a direct image comparison has been performed. [ABSTRACT FROM AUTHOR]

The ability to estimate future velocity on a road network is relevant for applications such as vehicle navigation systems and emergency vehicle dispatching. The existence of traffic congestion severely impacts travellers' travel time. In this paper, we investigate the use of congestion prediction in velocity forecasting models. Using a data-driven approach, we classify traffic observations into classes with and without congestion. We find that this classification improves velocity forecasting, showing that using congestion as an attribute reduces the MAE by at least 6.15% for different machine and deep learning models including random forests, multi-layer perceptrons and recurrent neural networks. We propose a random forest model that identifies the future congestion state from past traffic velocity and volume data and then use it to build new short-term velocity forecasting models. These models reduce the MAE prediction error up to 3.37% over the best models that do not consider congestion. This improvement represents overcoming a 53.75% of the error due to not precisely knowing the future congestion state. [ABSTRACT FROM AUTHOR]

Despite various supervised methods and algorithms, prediction methods do not necessarily provide the optimal values of the outputs. In this paper, an approach is proposed based on the integration of the clustering algorithm and a new mathematical programming model for predicting the efficient and optimal level of hospital outputs. In the first stage, units are evaluated using data envelopment analysis (DEA) and then, high-efficiency units are selected. In the second stage, the selected units are clustered based on the inputs of the units using the fuzzy C-means algorithm. For each unit to be estimated, the corresponding cluster is found and the closest unit to the target unit is determined. Finally, using the proposed new mathematical programming model, the optimal values are estimated based on the more efficient unit. A case study on Iranian hospitals illustrates the implementation of algorithms and methods and shows the abilities of the proposed approach. [ABSTRACT FROM AUTHOR]

In recent years, there are multiple temperature predictive models have demonstrated the effectiveness and outperformances of applying different deep neural architectures, such as convolutional neural network (CNN) and recurrent neural network (RNN) for the temperature forecasting task in forms of time-series analysis problem in comparing with previous traditional machine learning based techniques. However, up to this time, there are still several limitations of existing deep learning-based temperature predictive methods related to the capability of efficiently integrating extra information resources into the temperature data learning process. Moreover, the high-noised/chaotic fluctuations within daily temperature data also lead to downgrades in the accuracy performance of recent deep learning-based techniques. To overcome these challenges, in this paper, we proposed a novel integrated dual attention mechanism with the Convolutional Long Short-Term Memory Network (LSTM), called as: DAttConvLSTM. Our proposed DAttCovLSTM supports to effectively capture the chaotic and dynamic temporal information from daily temperature data, thus significantly improve the prediction results. Extensive experiments and comparative studies in real-world datasets demonstrated the effectiveness of our proposed model in comparing with recent state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

The efficient design of the aerodynamic surfaces in the aircraft allows the optimal performance of the vehicle and the reduction of the fuel consumption. Among these surfaces, the wing is the main contributor to the force which lifts the aircraft enabling the flight. However, the application of this force out of the center of gravity generates a moment that must be balanced through a force applied in the Horizontal-Tail-Plane (HTP) reaching the longitudinal trim of the aircraft. Traditionally, the design of the wing and HTP have been performed iteratively attaining suboptimal or time ineffective results. In this paper, we solve this problem through the application of a Genetic Algorithm for the combined optimization of the wing and the HTP by adjusting the aspect ratio, the taper ratio, the twist angle and the incidence angle of both surfaces to produce the optimal balance of lift adjusting its distribution to an elliptical configuration and enabling the longitudinal trim. Results show the automatic adjustment of the parameters of the aerodynamic surfaces thus fulfilling the objectives of this paper. [ABSTRACT FROM AUTHOR]

Due to the nonlinearity and high volatility of financial time series, hybrid forecasting systems combining linear and nonlinear models can provide more precise performance than a single model. Therefore, this study proposes a hybrid stock price forecasting model with error correction based on secondary decomposition. The modules of data decomposition, a prediction module and an error correction module constitute the overall framework of the model proposed in this paper. First, variational mode decomposition (VMD) decomposes the original stock closing price data and the initial prediction error sequence. Second, a gated recurrent unit (GRU) neural network model is selected to predict the original subseries. Finally, a new hybrid model consisting of the VMD method combined with an autoregressive integrated moving average (ARIMA) model is established to correct the error subseries. The proposed model is verified through four financial time series. The results show that (a) The combination of the VMD method and GRU network has a better prediction effect than a single model. (b) The combination of the VMD method and ARIMA method can effectively amend the error sequence and improve the prediction accuracy. (c) Error correction to the hybrid model has a great effect on improving the prediction performance of the model. The empirical results have illustrated that the proposed model indeed displays a good performance in forecasting stock market fluctuations compared with other baseline methods. [ABSTRACT FROM AUTHOR]

One of the necessities of mobile networks is uninterrupted access to wireless services, taking into account the requirements of quality of service. With the development of the fifth-generation (5G), high improvements will be achieved in several aspects such as seamless communications, flexibility, scalability, cost-effectiveness, and energy efficiency. For this purpose, in 5G architecture, a large amount of Small cell Base Stations (SBS) will be considered to cover all of the users and their equipment devices. Due to the possibility of rapid user mobility, the high traffic load on SBS, or any service failure, the handover process will be inevitable. However, there will be several challenges that must be noticed, such as selecting an appropriate SBS for handover in order to improve the quality of service and reduce unnecessary handovers. In this paper, a profile-based predictive handover strategy (PBPHS) is presented to select the best alternative for handover and hence to reduce handovers handovers, according to a predictive model that is based on the previous user's utilization of resources and mobility tracking. The simulation results depicted the performance of the proposed handover strategy in the handover reduction rate, which on average outperforms the state-of-the-art methods by 13% to 26%. [ABSTRACT FROM AUTHOR]

This paper proposes an ANN-based framework for forecasting the daily solar irradiance with the meteorological data collected over 10 years for Hisar city, located in Haryana. Meteorological information like temperature, relative humidity, and average wind speed is used as input variables for estimating sun irradiation in order to obtain high accuracy under various weather situations. Meteorological information is used as input variables for estimating the solar irradiation in order to obtain acceptable accuracy in a variety of weather circumstances. In addition, it was suggested in this paper that the Artificial Neural Network (ANN) model be trained using the Harris Hawks Optimization (HHO) Algorithm. The following task is to choose the best solar panel (SP) out of the thousands that are currently on the market. Due to a crucial component like weather, choosing the finest SP is important. Hence, this paper introduces a score-based model for selecting the best panel by considering the different criteria of the eleven SPs that are used as test panels. The annual power produced by panel P11 is 79.67% better than panel P1. Finally, based on the average power generated, the proposed panel P11 is regarded as the best panel when compared to other panels. [ABSTRACT FROM AUTHOR]

Due to the emergence of technologies in the growing world, the IoT is termed as a novel concept. The combination of internet with smart devices provides convenient user-related services. The attack toward confidential data may leak data and can cause serious security issues. Meanwhile, the classical security techniques cannot be utilized in a direct manner. Thus, novel techniques are needed for safeguarding the IoT from security issues. This paper designs secure authentication model for IoT in the healthcare. Several entities, such as gateway node, sensor node and medical professional are included for performing the secure authentication. The proposed protocol is devised by providing a mathematical model that makes usage of hashing function, encryption algorithm, XOR, Chebyshev polynomial, passwords, secret keys, and other security operations. Here, Secret key is generated with the deep neuro fuzzy network (DNFN) using Extreme Learning Machine (ELM) based sub-key generation. The proposed technique outperformed with less computation time of 33,484,154,235 ns, smallest memory of 51.750 MB, and highest detection rate of 94.9%. [ABSTRACT FROM AUTHOR]

Human chromosomes carry genetic information about our life. Chromosome classification is crucial for karyotype analysis. Existing chromosome classification methods do not take into account reasoning, such as: analyzing the relationship between variables, modeling uncertainty, and performing causal reasoning. In this paper, we introduce a knowledge engine for reasoning-based human chromosome classification that stores knowledge of chromosomes via a novel representation structure, the Chromosome Part Description (CPD), and reasons over CPDs by utilizing the probability tree model (PTM) for classification. Each CPD keeps information on a particular feature of chromosomes, while the PTM provides causal reasoning capability taking CPDs as nodes and dependencies between CPDs and types as edges. Experimental results show that the proposed knowledge engine's performance increases when providing more CPDs and achieves 100% classification accuracy with more than three CPDs. [ABSTRACT FROM AUTHOR]

Karyotyping requires chromosome instances to be segmented and classified from the metaphase images. One of the difficulties in chromosome segmentation is that the chromosomes are randomly positioned in the image, and there is a great chance for chromosomes to be touched or overlap with others. It is always much easier for operators and automatic programs to tackle images without overlapping chromosomes than ones with largely overlapped chromosomes. In order to reduce the processing difficulty, adding a smart image selection procedure ahead of segmentation is practical and necessary. In this paper, we introduce the Smart Karyotyping Image Selection (SKIS) based on Commonsense Knowledge Reasoning. The initial experiment demonstrates that the proposed approach can select the expected images based on reasoning and benefit following karyotyping processes. [ABSTRACT FROM AUTHOR]

Dynamic modern healthcare systems rely heavily on the contributions of computer scientists. The diagnosis process is a team effort involving many people: patients, their families, healthcare providers, researchers, and policymakers. Computer technology plays a crucial role in supporting this effort by providing a number of essential services to all of these groups. In the early stages of many diseases, a diagnosis can be made automatically using a computer-aided system, with some degree of certainty. This paper presents a hybrid optimal deep learning-based model for tuberculosis disease recognition using MRI images. Several deep learning models are combined to extract the most relevant features from MRI images. In particular, we establish a combination between vision transformer (ViTs) and Efficient-Net models in order to maximize classification accuracy. We conducted experiments to investigate the accuracy of the proposed model using the Shenzhen and Montgomery data set, and found that it yielded substantially more accurate and better results than the state of-the-art works. [ABSTRACT FROM AUTHOR]

This paper proposed an image retargeting quality assessment. In the proposed method, a deep convolution network is trained on the pixel displacement patterns of different image retargeting methods to produce a measure for evaluating the quality of output images. Also, the method extracts three other measures that assess the geometric changes of important objects in the image, the bending of block lines, and the extent of information loss during the retargeting process. The tests performed on two well-known databases, RetargetMe and CUHK, demonstrate the excellent performance, stability, and reliability of the proposed method compared to the existing methods. In this paper, we present a new method for evaluating the quality of retargeted images. The innovations of the proposed method are: Using a deep learning method to identify the important regions of the image that contain foreground objects and people Providing a quality evaluation measure, obtained by training a CNN with regression output on the pixel displacement patterns in different image retargeting methods Extracting the foreground objects in the original image and the corresponding objects in the retargeted image and determining the extent of geometric change in each object. Estimating the extent of information loss and distortion based on the extent to which the blocks of the original image have been bent. Estimating the quality score of the retargeted image using Gaussian process regression. [ABSTRACT FROM AUTHOR]

The article presents a preface to the issue, highlighting the common theme of knowledge management and otologies for intelligent system design in computer science.

One of the most significant subfields in "Synthetic Aperture Radar (SAR)" research is considered to be target detection. Numerous studies have been conducted on target identification, with the majority of them favoring filter-oriented methods. The fundamental goal of radar systems is to "detect moving targets on the ground." Decomposing a complex matrix into a structured sparse matrix and a low-rank matrix is a fundamental mathematics issue. Surveillance and reconnaissance rely heavily on "Ground Moving Target Indication (GMTI)," but it's not a simple task. The SAR ATI was first developed for calculating the radial velocity of ground-moving objects. Yet, overlapping stationary clutter can corrupt the recorded differential phase, resulting in mistakes in position and velocity calculations. The main concept of this paper is to propose a novel "Adaptive Simplified Fractional Fourier Transform (A-SFrFT)" using the intelligent meta-heuristic improvement. This adaptive SFrFT efficiently estimates the "Doppler parameters of the moving targets." The improved "Harris Hawks Optimization (HHO)" termed Trio Updating HHO (TU-HHO) is used as the meta-heuristic algorithm that enhances the performance of the SFrFT-based target estimation. The mathematical analysis and simulation findings show that the suggested methods recommended strategy is successful. [ABSTRACT FROM AUTHOR]

Iris recognition method is the most significant biometric modality for human identification. Currently, multiple deep structured architectures have been employed in biometric recognition with various features like robust generalization ability, high accuracy, and automatic learning. It is also considered as a more emerging area in biometrics along with machine learning approaches. Deep CNN is a conventional approach of image processing broadly utilized in several fields, however, it is simply influenced by slight disturbances and also includes bad anti-noise capacity in image classification. The major scope of this work is to implement adaptive deep learning-aided iris recognition. The key contribution of this paper relies on enhancing iris segmentation and iris recognition. Initially, the iris images are pre-processed, which are subjected to segmentation. The segmentation approach focuses on a Hough transform with K-Means Clustering (HT-KMC). Once the segmentation of the iris is done, the "Enhanced Faster Region-Convolutional Neural Network (E-Faster-RCNN)" is developed for the recognition purpose. The number of suitably hidden neurons of the conventional Faster-RCNN is optimized by the Adaptive-Sail Fish Optimization (ASFO) algorithm to obtain the highest efficiency. The simulation findings of the recommended approach proved that the method is effective in iris recognition by experimenting on a benchmark dataset. [ABSTRACT FROM AUTHOR]

Mental disorders are major problems and influence the thought of suicide, so it needs to be diagnosed and treated earlier. Detecting the symptoms of mental disorders is highly significant as they are considered a life-threatening task. Hence, this paper plans to implement new mental depression recognition by analyzing the tweet media text to secure the lives of the affected individuals. In the standard dataset, the text tweet data is collected. The gathered text data are used in the bags of n-grams and glove embedding for extracting the text features. Here, the extracted features are considered for the weighted feature selection, where the weight optimization takes place using the Awareness Probability-based Crow Black Widow Search algorithm. Finally, the weighted features are used for the recognition phase using the Convolutional Neural Network-Ensemble learning, where the final layer of CNN is replaced by the "Support Vector Machine (SVM), Random Forest (RF), Long Short Term Memory (LSTM) and Artificial Neural Network (ANN)" for classification. Here, parameter optimization occurs in the classifiers to improve the evaluation of the designed method. The experimental outcomes show the efficient performance of the developed method when compared with the other baseline approaches. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDS) are extensively employed for detecting suspicious behaviors in hosts. The ability of distributed IDS solutions makes it viable to combine and handle various kinds of sensors and generate alerts to different hosts positioned in distributed platforms. However, to offer secure and feasible services in a cloud platform is an imperative issue due to the impacts of attacks. This paper devises a novel IDS framework using cloud data to counter the influence of attacks. Here, the spark architecture is employed for discovering the intrusions. The pre-processing is applied to the input data for removing artifacts and noise considering input data. Thereafter, the feature extraction and feature fusion are performed in slave nodes. The feature fusion is carried out with the proposed Exponential Squirrel Search Algorithm (ExpSSA) algorithm. The fused features are considered in a deep-stacked autoencoder (Deep SAE) for performing effective intrusion detection. The proposed ExpSSA is adapted to train Deep SAE for tuning optimum weights. The exponential weighted moving average (EWMA) and squirrel search algorithm (SSA) are combined to create the proposed ExpSSA. The proposed ExpSSA-based Deep SAE offered improved performance compared to other techniques with the highest accuracy, detection rate of 0.846, and minimal FPR. [ABSTRACT FROM AUTHOR]

Cyberattacks are becoming more and more intense in the banking industry (Ryzhkova et al. 2020). Banking industry is trying to adopt artificial intelligence to create cyber defence system so that the unauthorized access and cyber attacks would be minimized. Banks in Qatar acknowledge the threat of cybercrimes and the role of cybersecurity in sustainable growth. Meanwhile, the banking industry is experiencing a major technological disruption. It becomes important to understand the implications of such technologies as artificial intelligence (AI) on the cybersecurity of banks. The present paper aims to explore the impacts of AI on the cybersecurity of banks in Qatar. A thematic analysis of interviews of 9 experts in the banking industry of Qatar was conducted. A qualitative thematic analysis was conducted using NVIVO 12 tool. Four key themes were identified: (1) AI is a major tool for enhancing the cybersecurity of banks in Qatar; (2) banks face challenges in using AI for improving cybersecurity; (3) AI can be used destructively and thus poses a threat to cybersecurity in banks of Qatar; (4) employed AI-based tools have vulnerabilities that can be exploited. Overall, Qatar banks can be expected to face new challenges in the future, due to changes in regulatory frameworks and increasing availability of AI-powered malware. [ABSTRACT FROM AUTHOR]

Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units. Previous works focus on low spatial resolution prediction, e.g., county-level, and preprocess data to the same geographic level, which loses some useful information. In this paper, we propose a fine-grained population mobility data-based model (FGC-COVID) utilizing data of two geographic levels for community-level COVID-19 prediction. We use the population mobility data between Census Block Groups (CBGs), which is a finer-grained geographic level than community, to build the graph and capture the dependencies between CBGs using graph neural networks (GNNs). To mine as finer-grained patterns as possible for prediction, a spatial weighted aggregation module is introduced to aggregate the embeddings of CBGs to community level based on their geographic affiliation and spatial autocorrelation. Extensive experiments on 300 days LA city COVID-19 data indicate our model outperforms existing forecasting models on community-level COVID-19 prediction. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the application of the OWA operator and some of its extensions in the calculation of continent and global tax revenues. The idea is to present how the analysis of an important economic indicator can vary depending on how the information is aggregated. An example was employed based on the Organization for Economic Co-operation and Development (OECD) database using 111 countries that were divided by continent, and then the global tax revenue was calculated using different aggregation operators. Different analyses can be carried out by governments and enterprises to improve decision making and fiscal politics. [ABSTRACT FROM AUTHOR]

Cooperation is an emerging paradigm for improving spatial differentiation in Vehicular Ad-Hoc Networks (VANETs). Hence, many network solutions are motivated through cooperative communications for boosting VANET efficiency regarding specific network constraints like energy efficiency, network capacity, and outage probability. The main intention of this paper is to design and develop an optimization model for selecting the minimum number of multi-hops between the source and destination for the cooperative VANET networks. The first phase has a first-time slot, where a signal is transmitted by the source to the cooperative nodes (its relays) and their equivalent destination. The main problem considered here is to optimally select the number of hops or relays that are adaptable for communication, to solve the multi-objective functions concerning the target throughput and outage probability. The adoption of new Dimension-based Cat Swarm Optimization (D-CSO) is the main contribution here to optimally select the multi-hops among source and destination. Through the performance evaluation, it is observed that the designed model has achieved networks-wide fairness performance and a good convergence rate. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of the Internet of Things (IoT) has made it possible to communicate and interact with various devices. In this regard, IoT data has tremendous volume, veracity, variety and velocity known as big data. Due to the complexity and heterogeneity of this data, classical data analysis approaches to compatibility with IoT applications are not valid at all levels. Although the cloud can temporarily store big data to reduce the complexity of the analysis process, but problems such as security and latency persist. Machine learning techniques have shown promising results in previous studies for IoT data processing. Big data analytics methods can also be used to improve the performance of IoT applications and related challenges. Therefore, the use of machine learning techniques and big data technologies can achieve better performance and management of IoT applications. The purpose of this paper is to review machine learning techniques and recent advances in big data analytics that can be used to develop IoT applications. This study seeks to achieve this purpose by reviewing published articles on IoT-based machine learning and IoT-based big data between 2010 and 2021. There are several challenges and solutions in the literature for these fields, so that 32 articles have been reviewed to address these issues. These articles are reviewed in four general categories: platform, framework, applications and model to be the starting point for future research. [ABSTRACT FROM AUTHOR]