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Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified into normal, benign, and malignant types using the Adaptive Neuro- Fuzzy Inference System (ANFIS) approach in this paper. This mammogram classification process consists of a noise filtering module, spatial-frequency transformation module, feature computation module, and classification module. The Gaussian Filtering Algorithm (GFA) is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation module. The statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by theANFIS technique in this paper. Finally, Probability Histogram Segmentation Algorithm (PHSA) is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram images. This proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM datasets. From the extensive analysis of the proposed tumor detection methods stated in this work with other works, the proposed work significantly achieves a higher performance. The methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions. [ABSTRACT FROM AUTHOR]

The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time. Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time. In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers. The main objective of the work is to minimize the gap in the total size of assigned files between compilers. This minimization ensures the fair distribution of files to different compilers. This problem is considered to be a very hard problem. This paper presents two research axes. The first axis is related to architecture. We propose a novel pre-compiler architecture in this context. The second axis is algorithmic development. We develop six algorithms to solve the problem, in this context. These algorithms are based on the dispatching rules method, decomposition method, and an iterative approach. These algorithms give approximate solutions for the studied problem. An experimental result is implemented to show the performance of algorithms. Several indicators are used to measure the performance of the proposed algorithms. In addition, five classes are proposed to test the algorithms with a total of 2350 instances. A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm. The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic (ISL) with a percentage equal to 97.7% and an average running time equal to 0.148 s. All other algorithms did not exceed 22% as a percentage. The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic (ILS) with a percentage equal to 21,4% and an average running time equal to 0.150 s. [ABSTRACT FROM AUTHOR]

Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. In a cloud-based context, this paper describes load balancing measurements, including the benefits and drawbacks associated with the selected load balancing techniques. It also summarizes the algorithms based on implementation, time complexity, adaptability, associated issue(s), and targeted QoS parameters. Additionally, the analysis evaluates the tools and instruments utilized in each investigated study. Moreover, comparative analysis among static, traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed. Finally, the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed. [ABSTRACT FROM AUTHOR]

Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password. Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers. This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services. The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users' information. The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity. The research study introduces a novel algorithm called "Bio_Authen_as_a_Service" for implementing and evaluating the proposed model. The proposed system performs the biometric authentication process securely and preserves the privacy of user information. The experimental result was highly promising for securing cloud services using the proposed model. The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%. [ABSTRACT FROM AUTHOR]

Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the random walk strategy is improved by integrating the degree information and label information of nodes. Then, the node sequence obtained from random walk sampling is transformed into the node representation vector by the Skip-Gram model. At last, the experimental results on ten real-world networks demonstrate that the proposed algorithm has great advantages in the label classification, network reconstruction and link prediction tasks, compared with three benchmark algorithms. [ABSTRACT FROM AUTHOR]

Aeroengine surge fault is one of the main causes of flight accidents. When a surge occurs, it is hard to detect it in time and take anti-surge measures correctly. Recently, people have been applying detection methods based on mathematical models and expert knowledge. Due to difficult modeling and limited failure-mode coverage of these methods, early surge detection cannot be achieved. To address these problems, firstly, this paper introduced the data of six main sensors related to the aeroengine surge fault, such as, total pressure at compressor (high pressure rotor) outlet (Pt3), high pressure compressor rotor speed (N2), power level angle (PLA), etc. Secondly, aiming at preprocessing of data sets, this paper proposed a data standardization preprocessing algorithm based on batch sliding window (DSPABSW) to build a training set, validation set and test set. Thirdly, aeroengine surge fault detection fusion neural network (ASFDFNN) was provided and optimized to improve the detection accuracy of aeroengine surge faults. Finally, the experimental results showed that the model achieved 95.7%, 93.6% and 94.7% in precision rate, recall rate and F1_Score respectively and consequently it can detect the aeroengine surge fault 260 ms in advance. [ABSTRACT FROM AUTHOR]

The traditional level set algorithm selects the position of the initial contour randomly and lacks the processing of edge information. Therefore, it cannot accurately extract the edge of the brain tissue. In order to solve this problem, this paper proposes a level set algorithm that fuses partition and Canny function. Firstly, the idea of partition is fused, and the initial contour position is selected by combining the morphological information of each region, so that the initial contour contains more brain tissue regions, and the efficiency of brain tissue extraction is improved. Secondly, the canny operator is fused in the energy functional, which improves the accuracy of edge detection of rhesus monkey brain tissue while retaining the advantage of the traditional level set algorithm in processing an uneven gray image. Experimental results show that the algorithm can accurately extract the brain tissue of rhesus monkeys with an accuracy of up to 86%. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is proposed to solve the needs of Internet of Things (IoT) users for high resource utilization, high reliability and low latency of service requests. However, the backup virtual machine is idle when its primary virtual machine is running normally, which will waste resources. Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization. First, these virtual machines are deployed into slots randomly, and then some tasks with cooperative relationship are offloaded to virtual machines for processing. Different deployment locations have different resource utilization and average service response time. We want to find a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization. In this paper, we propose a task scheduler and exploit a Task Deployment Algorithm (TDA) to obtain an optimal virtual machine deployment scheme. Finally, the simulation results show that the TDA can significantly increase the resource utilization of the system, while reducing the average service response time of the application by comparing TDA with the other two classical methods. The experimental results confirm that the performance of TDA is better than that of other two methods. [ABSTRACT FROM AUTHOR]

In the augmented reality system, the position and direction of the user's point of view and line of sight in the real scene is acquired in real-time. The position and direction information will determine the exact position of the virtual object of the real scene. At the same time, various coordinate systems are established according to the user's line of sight. So registration tracking technology is very important. The paper proposes an accurate, stable, and effective augmented reality registration algorithm. The method adopts the method of ORB (oriented FAST and rotated BRIEF) features matching combined with RANSAC (random sample consensus) to obtain the homography matrix and then uses the KLT (kanade-lucas-tomasi) tracking algorithm to track the mark, which is a better solution. The error accumulation defect based on the natural feature tracking registration method is improved, and the stability and accuracy of the registration are improved. Experiments have proved that the algorithm in this paper is accurate, stable, and effective, and can complete the virtual and real registration tasks accurately and stably even when the marked part is not visible. [ABSTRACT FROM AUTHOR]

High-dimensional datasets present significant challenges for classification tasks. Dimensionality reduction, a crucial aspect of data preprocessing, has gained substantial attention due to its ability to improve classification performance. However, identifying the optimal features within high-dimensional datasets remains a computationally demanding task, necessitating the use of efficient algorithms. This paper introduces the Arithmetic Optimization Algorithm (AOA), a novel approach for finding the optimal feature subset. AOA is specifically modified to address feature selection problems based on a transfer function. Additionally, two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision, slow convergence, and susceptibility to local optima. The first enhancement proposes a new method for selecting solutions to be improved during the search process. This method effectively improves the original algorithm's accuracy and convergence speed. The second enhancement introduces a local search with neighborhood strategies (AOA_NBH) during the AOA exploitation phase. AOA_NBH explores the vast search space, aiding the algorithm in escaping local optima. Our results demonstrate that incorporating neighborhood methods enhances the output and achieves significant improvement over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multi-Agent Reinforcement Learning (MARL) has proven to be successful in cooperative assignments. MARL is used to investigate how autonomous agents with the same interests can connect and act in one team. MARL cooperation scenarios are explored in recreational cooperative augmented reality environments, as well as realworld scenarios in robotics. In this paper, we explore the realm of MARL and its potential applications in cooperative assignments. Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory withminimal damage. To accomplish this, we utilize the StarCraftMulti-Agent Challenge (SMAC) environment and train four MARL algorithms: Q-learning with Mixtures of Experts (QMIX), Value-DecompositionNetwork (VDN), Multi-agent Proximal PolicyOptimizer (MAPPO), andMulti-Agent Actor Attention Critic (MAA2C). These algorithms allow multiple agents to cooperate in a specific scenario to achieve the targeted mission. Our results show that the QMIX algorithm outperforms the other three algorithms in the attacking scenario, while the VDN algorithm achieves the best results in the defending scenario. Specifically, the VDNalgorithmreaches the highest value of battlewonmean and the lowest value of dead alliesmean. Our research demonstrates the potential forMARL algorithms to be used in real-world applications, such as controllingmultiple robots to provide helpful services or coordinating teams of agents to accomplish tasks that would be impossible for a human to do. The SMACenvironment provides a unique opportunity to test and evaluateMARL algorithms in a challenging and dynamic environment, and our results show that these algorithms can be used to achieve victory with minimal damage. [ABSTRACT FROM AUTHOR]

An autonomous robot is often in a situation to perform tasks or missions in an initially unknown environment. A logical approach to doing this implies discovering the environment by the incremental principle defined by the applied exploration strategy. A large number of exploration strategies apply the technique of selecting the next robot position between candidate locations on the frontier between the unknown and the known parts of the environment using the function that combines different criteria. The exploration strategies based on Multi-Criteria Decision-Making (MCDM) using the standard SAW, COPRAS and TOPSIS methods are presented in the paper. Their performances are evaluated in terms of the analysis and comparison of the influence that each one of them has on the efficiency of exploration in environments with a different risk level of a “bad choice” in the selection of the next robot position. The simulation results show that, due to its characteristics related to the intention to minimize risk, the application of TOPSIS can provide a good exploration strategy in environments with a high level of considered risk. No significant difference is found in the application of the analyzed MCDM methods in the exploration of environments with a low level of considered risk. Also, the results confirm that MCDM-based exploration strategies achieve better results than strategies when only one criterion is used, regardless of the characteristics of the environment. The famous D* Lite algorithm is used for path planning. [ABSTRACT FROM AUTHOR]

In existing meta-heuristic algorithms, population initialization forms a huge part towards problem optimization. These calculations can impact variety and combination to locate a productive ideal arrangement. Especially, for perceiving the significance of variety and intermingling, different specialists have attempted to improve the presentation of meta-heuristic algorithms. Particle Swarm Optimization (PSO) algorithm is a populace-based, shrewd stochastic inquiry strategy that is motivated by the inherent honey bee swarm food search mechanism. Population initialization is an indispensable factor in the PSO algorithm. To improve the variety and combination factors, rather than applying the irregular circulation for the introduction of the populace, semi-arbitrary successions are more helpful. This examination presents a thorough overview of the different PSO initialization approaches which are dependent on semi-arbitrary successions systems. In this precise review, the best in class in the populace instatement is uncovered. The procedures are classified by utilizing a theoretical model that parts the cycle of populace introduction into two phases: that is, right now expressly or certainly utilized for reinstatement in every single present approach. The deliberate investigation unveils the potential examination zones of populace introduction and, furthermore, research holes, despite the fact that the fundamental center is to give the headings to future upgrade and advancement around there. This paper gives a deliberate study identified with this calculated model for the cutting edge of exploration, which is talked about in the predefined writing to date. The study is envisioned to be useful in examining the PSO algorithm in detail for the specialist. Likewise, the paper finds the proficiency of numerous quasi-random sequences (QRS) based on initialization approaches by looking at their exhibition analyzed for sixteen notable benchmark test problems. [ABSTRACT FROM AUTHOR]

Test case generation and optimization is the foremost requirement of software evolution and test automation. In this paper, a bio-inspired Crow Search Algorithm (CSA) is suggested with an improved objective function to fulfill this requirement. CSA is a nature-inspired optimization method. The improved objective function combines branch distance and predicate distance to cover the critical path on the control flow graph. CSA is a search-based technique that uses heuristic information for automation testing, and CSA optimizers minimize test cases generated by satisfying the objective function. This paper focuses on generating test cases for all paths, including critical paths. The control flow graph covers the information flow among all the classes, functions, and conditional statements and provides test paths. The number of test cases examined through graph path coverage analysis. The minimum number of test paths is counted through complexity metrics using the cyclomatic complexity of the constructed graph. The proposed method is evaluated as mathematical optimization functions to validate their effectiveness in locating optimal solutions. The python codes are considered for evaluation and revealed that our approach is time-efficient and outperforms various optimization algorithms. The proposed approach achieved 100% path coverage, and the algorithm executes and gives optimum results in approximately 0.2745 seconds. [ABSTRACT FROM AUTHOR]

Although the current Domain Name System (DNS) has been able to satisfy the use of network services, there are still many challenges in the future development of the Internet. The centralized management of traditional domain name management systems has many risks, and cannot defend against Distributed Denial of Service (DDoS) attacks and single points of failure. As a decentralized tool, blockchain provides innovative ideas for the improvement of domain name management systems. Starting from the existing network resolution system and combining the application of cross-chain communication in DNS, this paper proposes a domain name resolution service architecture model based on the master- slave chain, adopts a Multi-Sig Notary Schemes to achieve cross-chain communication, and proposes based on the master-slave chain consensus algorithm (MSBFT) of the PBFT algorithm. In a single blockchain, this paper uses the VRF algorithm to select the master node. Finally, the simulation experiment designed in this paper tests the throughput and analysis delay of the analytical model, compares the analytical delay of the main chain and slave chain, and analyzes the advantages of the model. [ABSTRACT FROM AUTHOR]

The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage is not properly managed it will become a hazard to the environment and humans. Managing medical wastage is a major issue in the city, municipalities in the aspects of the environment, and logistics. An efficient supply chain with edge computing technology is used in managing medical waste. The supply chain operations include processing of waste collection, transportation, and disposal of waste. Many research works have been applied to improve the management of wastage. The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security, trustworthiness, and transparency. To overcome these issues, in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm (NBQ-BBOA). This proposed work is used to track, detect, and manage medical waste. To minimize the transferring cost of medical wastage from various nodes, the Q-Learning algorithm is used. The accuracy obtained for the Naïve Bayes algorithm is 88%, the Q-Learning algorithm is 82% and NBQ-BBOA is 98%. The error rate of Root Mean Square Error (RMSE) and Mean Error (MAE) for the proposed work NBQ-BBOA are 0.012 and 0.045. [ABSTRACT FROM AUTHOR]

An NP-hard problem like Flexible Job Shop Scheduling (FJSP) tends to be more complex and requires more computational effort to optimize the objectives with contradictory measures. This paper aims to address the FJSP problem with combined and contradictory objectives, like minimization of make-span, maximum workload, and total workload. This paper proposes ‘Hybrid Adaptive Firefly Algorithm’ (HAdFA), a new enhanced version of the classic Firefly Algorithm (FA) embedded with adaptive parameters to optimize the multi objectives concurrently. The proposed algorithm has adopted two adaptive strategies, i.e., an adaptive randomization parameter (α) and an effective heterogeneous update rule for fireflies. The adaptations proposed by this paper can help the optimization process to strike a balance between diversification and intensification. Further, an enhanced local search algorithm, Simulated Annealing (SA), is hybridized with Adaptive FA to explore the local solution space more efficiently. This paper has also attempted to solve FJSP by a rarely used integrated approach where assignment and sequencing are done simultaneously. Empirical simulations on benchmark instances demonstrate the efficacy of our proposed algorithms, thus providing a competitive edge over other nature-inspired algorithms to solve FJSP. [ABSTRACT FROM AUTHOR]

Face authentication is an important biometric authentication method commonly used in security applications. It is vulnerable to different types of attacks that use authorized users' facial images and videos captured from social media to perform spoofing attacks and dynamic movements for penetrating security applications. This paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble technique. The proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a specific response for every authentication trial with a different sequence of emotions. The proposed model is applied to the KDEF dataset using 10-fold cross-validations. Several improvements are made to the proposed model. First, the VGG16 model is applied to the seven common emotions. Second, the system usability is enhanced by analyzing and selecting only the four common and easy-to-use emotions. Third, the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authentication processes. Finally, the Horizontal Ensemble Best N-Losses (HEBNL) is applied using challenge-response emotion to improve the authentication efficiency and minimize the computational power. The successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1% to 99.27%. [ABSTRACT FROM AUTHOR]

Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process. Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process. This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research. A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms. An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms. An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically. The experimental results are conducted on two datasets to measure the overall accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms. [ABSTRACT FROM AUTHOR]

In recent years, due to the popularization of deep learning technology, more and more attention has been paid to the security of deep neural networks. A wide variety of machine learning algorithms can attack neural networks and make its classification and judgement of target samples wrong. However, the previous attack algorithms are based on the calculation of the corresponding model to generate unique adversarial examples, and cannot extract attack features and generate corresponding samples in batches. In this paper, Generative Adversarial Networks (GAN) is used to learn the distribution of adversarial examples generated by FGSM and establish a generation model, thus generating corresponding adversarial examples in batches. The experiment shows that using the Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (DCGAN) to extract and learn the attack characteristics from the FGSM algorithm, the generated adversarial examples attacked the original model with a success rate of 89.1%. For the model attack with increased protection, the success rate increased by 30.3%. This suggests that the adversarial examples generated by GAN are more effective and aggressive. This paper proposes a new approach to generate adversarial examples. [ABSTRACT FROM AUTHOR]

With the rapid development of network media, the short text has become the main cover of information dissemination by quickly disseminating relevant entity information. However, the lack of context in the short text can easily lead to ambiguity, which will greatly reduce the efficiency of obtaining information and seriously affect the user's experience, especially in the financial field. This paper proposed an entity disambiguation algorithm based on multiword vector ensemble and decision to eliminate the ambiguity of entities and purify text information in information processing. First of all, we integrate a variety of unsupervised pre-trained word vector models as vector embeddings according to different word vector models' characteristics. Moreover, we use the classic architecture of long short-term memory (LSTM) combined with the convolutional neural network (CNN) to fine-tune pre-trained Chinese word vectors such as BERT to integrate the output of entity recognition results. Then build the knowledge base and introduce the focal loss function on the basis of CNN and binary classification to improve the effect of entity disambiguation. Experimental results show that the algorithm performs better than the traditional entity disambiguation algorithm based on the single word vector. This method can accurately locate the entity to be disambiguated and has a good performance in disambiguation accuracy. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems utilize maximum power point tracking (MPPT) controllers to optimize power output amidst varying environmental conditions. However, the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation. Under partial shade conditions, the global maximum power point (GMPP) may be missed by most traditional maximum power point tracker. The flower pollination algorithm (FPA) and particle swarm optimization (PSO) are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP. This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems. The first issue is that the initial values of pollen are determined randomly at first, which can lead to premature convergence. To minimize the convergence time and enhance the possibility of detecting the GMPP, the initial pollen values were modified so that they were near the expected peak positions. Secondly, in the modified FPA, population fitness and switch probability values both influence swapping between two-mode optimization, which may improve the flower pollination algorithm's tracking speed. The performance of the modified flower pollination algorithm (MFPA) is assessed through a comparison with the perturb and observe (P&O) method and the standard FPA method. The simulation results reveal that under different partial shading conditions, the tracking time for MFPA is 0.24, 0.24, 0.22, and 0.23 s, while for FPA, it is 0.4, 0.35, 0.45, and 0.37 s. Additionally, the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions, with values of 99.98%, 99.90%, 99.93%, and 99.26%, compared to FPA with MPPT efficiencies of 99.93%, 99.88%, 99.91%, and 99.18%. Based on the findings from simulations, the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions. [ABSTRACT FROM AUTHOR]

Localmarkets in East Africa have been destroyed by raging fires, leading to the loss of life and property in the nearby communities. Electrical circuits, arson, and neglected charcoal stoves are the major causes of these fires. Previous methods, i.e., satellites, are expensive to maintain and cause unnecessary delays. Also, unit-smoke detectors are highly prone to false alerts. In this paper, an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach. A free open--source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model. Two crisp input parameters, namely: FIT and FIG are used. Results show that the Interval Type-2 model achieved an accuracy value of FIO =98.2%, MAE=1.3010, MSE=1.6938 and RMSE=1.3015 using regression analysis. The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger. As a result, the proposed solution can be fully implemented in low-cost, low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts. Through informed decision-making in low-cost fire detection devices, early warning notifications can be provided to aid in the rapid evacuation of people, thereby improving fire safety surveillance, management, and protection for the market community. [ABSTRACT FROM AUTHOR]

Berth and loading and unloading machinery are not only the main factors that affecting the terminal operation, but also the main starting point of energy saving and emission reduction. In this paper, a genetic Algorithm Framework is designed for the berth allocation with low carbon and high efficiency at bulk terminal. In solving the problem, the scheduler's experience is transformed into a regular way to obtain the initial solution. The individual is represented as a chromosome, and the sub-chromosomes are encoded as integers, the roulette wheel method is used for selection, the two-point crossing method is used for cross, and the exchange variation method is used for variation in the procedure of designing the Algorithm. Considering the complexity of berth scheduling problem and the diversity of constraints and boundary conditions, the genetic algorithm combines with system simulation to get the final scheme of berth allocation. This model and algorithm are verified to be practical by analyzing multiple sets of examples of shorelines with different lengths. When compared with the traditional algorithms in three aspects which includes berth offset distance, departure delay cost and energy consumption of portal crane, the result indicates that the improved algorithm is more effective and feasible. The study will help to lower energy consumption and resource waste, reduce environmental pollution, and provide a reference for low-carbon, green and sustainable development of the terminal. [ABSTRACT FROM AUTHOR]

There is a large amount of information in the network data that we can exploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem. Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning. Therefore, we propose an improved density peak clustering algorithm (ILDPC) for the community detection problem, which improves the local density mechanism in the original algorithm and can better accommodate class clusters of different shapes. And we study the community detection in network data. The algorithm is paired with the benchmark model Graph sample and aggregate (GraphSAGE) to show the adaptability of ILDPC for community detection. The plotted decision diagram shows that the ILDPC algorithm is more discriminative in selecting density peak points compared to the original algorithm. Finally, the performance of K-means and other clustering algorithms on this benchmark model is compared, and the algorithm is proved to be more suitable for community detection in sparse networks with the benchmark model on the evaluation criterion F1-score. The sensitivity of the parameters of the ILDPC algorithm to the low-dimensional vector set output by the benchmark model GraphSAGE is also analyzed. [ABSTRACT FROM AUTHOR]

The delivery of combined benefits of Alternating Current (AC) motor and Direct Current (DC) Motor makes the Brushless Direct Current (BLDC) motors as a unique feature in numerous industrial applications. The possibilities of running the motor at very high speed with extensive operating life span of BLDC with miniature and its compact design make it an un-ignorable option for Electrical Engineers. With many advantages, till managing as well as controlling the speed of BLDC is complicated. This work is intended to come up with an effective control of speed of the motor through Torque Ripple Minimization Route and an Enhanced-Fractional Order Proportional Integral and Derivative Controller based Ant-Lion Optimizer (EFCA)Technique, which is evaluated as well as its strategy is executed in this paper. The critical factors like BLDC's Back Electro Magnetic Force, Values of Current, Torque level and the operating speed of the motor are considered and evaluated closely and from that, the ripple level present in the torque is controlled as well as it is brought down to minimalistic value by using Fractional Order (FO) Proportional Integral Derivative (FOPID) controller. To control the speed of the motor, the Gain parameters and the ripple present in the torque have to be reduced, the latter is obtained using FOPID controller and the former is achieved by employing Modified - Ant Lion Optimizer (ALO). The technique presented in this paper helps for the enhancement of controlling behavior of BLDC and improves the overall performance of the motor. The dynamic part of the BLDC is analyzed using the platform of Matrix Laboratory (MATLAB) simulink and the results are tabulated. The final values are compared with the results of the existing methodologies and the comparative study has yielded that the presented technique has outperformed than that of the available methodologies. A proto type model has been designed and results are executed in real time environment to ensure the effectiveness of the presented work. [ABSTRACT FROM AUTHOR]

The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects due to its ability to measure the facial transient temperature, which is correlated with human affects and robustness against illumination changes. Therefore, studies have increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation of light conditions and revealing original human affect. Moreover, the thermal-based imaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and noninvasive way. This paper presents a brief review on human affects and focuses on the advantages and challenges of the thermal imaging technique. In addition, this paper discusses the stages of thermal-based human affective state recognition, such as dataset type, preprocessing stage, region of interest (ROI), feature descriptors, and classification approaches with a brief performance analysis based on a number of works in the literature. This analysis could help beginners in the thermal imaging and affective recognition domain to explore numerous approaches used by researchers to construct an affective state system based on thermal imaging. [ABSTRACT FROM AUTHOR]

CC's (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets. The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers. The proposed AKFA (Adaptive Kernel Firefly Algorithm) is for selecting features for CNM (Cloud Network Monitoring) operations. AKFA methodology is demonstrated using CNSD (Cloud Network Security Dataset) with satisfactory results in the performance metrics like precision, recall, F-measure and accuracy used. [ABSTRACT FROM AUTHOR]

With the growth of the discipline of digital communication, the topic has acquiredmore attention in the cybersecuritymedium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)--is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the RSA's structure. Thus, the CM can effectively expand search space and enhance the performance of the RSA. The developed RSA-CM is assessed on five publicly available ID datasets: KDD-CUP99, NSL-KDD, UNSW-NB15, CIC-IDS2017, and CIC-IDS2018 and two engineering problems. The RSA-CM is compared with the original RSA, and three other state-of-the-art FS methods, namely particle swarm optimization, grey wolf optimization, and multi-verse optimizer, and quantitatively is evaluated using fitness value, the number of selected optimum features, accuracy, precision, recall, and F1- score evaluationmeasures. The results reveal that the developed RSA-CMgot better results than the other competitive methods applied for FS on the ID datasets and the examined engineering problems. Moreover, the Friedman test results confirm that RSA-CMhas a significant superiority compared to other methods as an FS method for ID. [ABSTRACT FROM AUTHOR]

Interconnected devices and intelligent applications have slashed human intervention in the Internet of Things (IoT), making it possible to accomplish tasks with less human interaction. However, it faces many problems, including lower capacity links, energy utilization, enhancement of resources and limited resources due to its openness, heterogeneity, limited resources and extensiveness. It is challenging to route packets in such a constrained environment. In an IoT network constrained by limited resources, minimal routing control overhead is required without packet loss. Such constrained environments can be improved through the optimal routing protocol. It is challenging to route packets in such a constrained environment. Thus, this work is motivated to present an efficient routing protocol for enhancing the lifetime of the IoT network. Lightweight On-demand Ad hoc Distance-vector Routing Protocol--Next Generation (LOADng) protocol is an extended version of the Ad Hoc On-Demand Distance Vector (AODV) protocol. Unlike AODV, LOADng is a lighter version that forbids the intermediate nodes on the route to send a route reply (RREP) for the route request (RREQ), which originated from the source. A resource-constrained IoT network demands minimal routing control overhead and faster packet delivery. So, in this paper, the parameters of the LOADng routing protocol are optimized using the black widow optimization (BWO) algorithm to reduce the control overhead and delay. Furthermore, the performance of the proposed model is analyzed with the default LOADng in terms of delay, delivery ratio and overhead. Obtained results show that the LOADng-BWO protocol outperforms the conventional LOADng protocol. [ABSTRACT FROM AUTHOR]

Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms. However, there is no unsupervised interference signals recognition algorithm at present. In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed. Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning. In the second phase, the original dataset's k-nearest neighbor set (KNNset) is designed in double dimensions contrastive learning. In addition, a dynamic entropy parameter strategy is proposed. The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies; the feature dimensional contrastive learning in the second phase can improve the clustering purity; the dynamic entropy parameter strategy can improve the stability of DDCC effectively. The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms. In particular, the clustering purity of our method is above 92%, SCAN's is 81%, and the other three methods' are below 71% when jammingnoise-ratio (JNR) is -5 dB. In addition, our method is close to the supervised learning algorithm. [ABSTRACT FROM AUTHOR]

The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the selection of low-level heuristic strategies. Secondly, a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities. Besides, we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process. Compared with HHSA, ACO, GA, F-PSO, etc, HHRL can quickly obtain task complexity, select appropriate heuristic strategies for task scheduling, search for the the best makspan and have stronger disturbance detection ability for population diversity. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to MANET's potential to provide small-cost solutions for real-world contact challenges, decentralized management, and restricted bandwidth, MANETs are more vulnerable to security threats. When protecting MANETs from attack, encryption and authentication schemes have their limits. However, deep learning (DL) approaches in intrusion detection systems (IDS) can adapt to the changing environment of MANETs and allow a system to make intrusion decisions while learning about its mobility in the environment. IDSs are a secondary defiance system for mobile ad-hoc networks vs. attacks since they monitor network traffic and report anything unusual. Recently, many scientists have employed deep neural networks (DNNs) to address intrusion detection concerns. This paper used MANET to recognize complex patterns by focusing on security standards through efficiency determination and identifying malicious nodes, and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network (CBPNN), Feedforward-Neural-Network (FNN), and Cascading-Back-Propagation-Neural- Network (CBPNN) (FFNN). In addition to Convolutional-Neural-Network (CNN), these primary forms of deep neural network (DNN) building designs are widely used to improve the performance of intrusion detection systems (IDS) and the use of IDS in conjunction with machine learning (ML). Furthermore, machine learning (ML) techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to different environments. Compared with another current model, The proposed model has better average receiving packet (ARP) and end-to-end (E2E) performance. The results have been obtained from CBP, FFNN and CNN 74%, 82% and 85%, respectively, by the time (27, 18, and 17 s). [ABSTRACT FROM AUTHOR]

Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distancemetric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this paper, to solve the clustering problem in the few supervised sample and high data dimensionality scenarios, a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information, such as Must-Link and Cannot-Link, and then cluster the data in the new metric space. The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping. Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm. Average clustering metrics on various datasets improved by 8% compared to the comparison algorithm. [ABSTRACT FROM AUTHOR]

In real-time contents, such as games and interactive simulators, it is very important to reduce the amount of simulation computation of 3D deformable objects. Although position-based dynamics has been proposed to reduce the amount of computation, the number of nodes for the tetrahedral model to represent a volumetric deformable object has to be increased, which makes the real-time simulation difficult. Therefore, this paper proposes an Internal shape preserving constraint(ISPC) generation algorithm integrated into the positionbased dynamics to represent the physical properties of the 3D volumetric deformable object, while reducing the number of nodes filling the interior of the object. The proposed algorithm not only provides motion behavior similar to the tetrahedral model by using a surface model, but also enables real-time simulation by reducing the number of nodes constituting the 3D virtual object. It showed high FPS with reduced computation time compared to the tetrahedral model, and the volume maintenance and physical properties of model were expressed similarly to the tetrahedral model. [ABSTRACT FROM AUTHOR]

Calculating the most reliable maximum flow (MRMF) from the edge cache node to the requesting node can provide an important reference for selecting the best edge cache node in a content delivery network (CDN). However, SDBA, as the current state-of-the-art MRMF algorithm, is too complex to meet real-time computing needs. This paper proposes a set of MRMF algorithms: NWCD (Negative Weight Community Deletion), SCPDAT (Single-Cycle Preference Deletion Approximation algorithm with Time constraint) and SCPDAP (Single-Cycle Preference Deletion Approximation algorithm with Probability constraint). NWCD draws on the "flow-shifting" algorithm of minimum cost and maximum flow, and further defines the concept of negative weight community. This algorithm continuously deletes the negative weight communities, which can increase reliability while keeping the flow constant in the residual graph. It is proven that when all negative weight communities are deleted, the corresponding maximum flow is the MRMF. SCPDAT tries to approach the optimal solution to the greatest extent possible within the limited time, while SCPDAP tries to reach the probability threshold in the shortest amount of time. Both of these adopt the strategy of first deleting single-cycle communities (which contribute more to the reliability with lower time cost). Experiments show that, compared with SDBA, NWCD combined with the probabilistic pruning achieves an order of magnitude improvement in time cost, while SCPDAT and SCPDAP demonstrate better time performance and increased applicability. [ABSTRACT FROM AUTHOR]

Automated biometric authentication attracts the attention of researchers to work on hand-based images to develop applications in forensics science. Finger Knuckle Print (FKP) is one of the hand-based biometrics used in the recognition of an individual. FKP is rich in texture, less in contact and known for its unique features. The dimensionality of the features, extracted from the image, is one of the main problems in pattern recognition. Since selecting the relevant features is an important but challenging task, the feature subset selection is an optimization problem. A reduced number of features results in enhanced classification accuracy. The proposed FKP system presents a mulitalgorithm fusion based on subspace algorithms at feature level fusion technique. In this paper, a new feature- selection algorithm, which is a Modified Magnetotatic bacterium Optimization Algorithm (MMBOA), is proposed for finger knuckle recognition to select relevant and useful features that increase the classification accuracy. The distinct characteristic of this bacterium influences the design of a new optimization technique. The hybrid features such as Eigen and Fisher (EiFi) are extracted from the finger knuckle. The fusion of this feature vector is optimized using newly proposed MMBOA_mr optimization algorithm. The results demonstrate a significant improvement compared with unimodal identifiers, and the proposed approach significantly outperforms with a recognition accuracy of 99.7% with 22 features with the reduction rate of 72%. Additionally, the proposed approach is compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recent progress on image colorization is substantial and benefiting mostly from the great development of the deep convolutional neural networks. However, one type of object can be colored by different kinds of colors. Due to the uncertain relationship between the object and color, the deep neural network is unstable and difficult to converge during the training process. In order to solve this problem, this paper proposes an instance-aware automatic image colorization algorithm, which uses the semantic features of the object instance as prior knowledge to guide the deep neural network to do the colorization task. Meanwhile, we design a discrete loss function to train the deep network and this network can be trained from end to end. Experiments show that this algorithm can obtain satisfactory colorful results on the images containing object instance and achieves state-of-the-art results. [ABSTRACT FROM AUTHOR]

In the paper, we apply the sparse reconstruction algorithm of improved background dictionary to saliency detection. Firstly, after super-pixel segmentation, two bottom features are extracted: the color information of LAB and the texture features of the image by Gabor filter. Secondly, the convex hull theory is used to remove object region in boundary region, and K-means clustering algorithm is used to continue to simplify the background dictionary. Finally, the saliency map is obtained by calculating the reconstruction error. Compared with the mainstream algorithms, the accuracy and efficiency of this algorithm are better than those of other algorithms. [ABSTRACT FROM AUTHOR]

A large number of new words in product reviews generated bymobile terminals are valuable indicators of the privacy preferences of customers. By clustering these privacy preferences, sufficient information can be collected to characterize users and provide a data basis for the research issues of privacy protection. The widespread use of mobile clients shortens the string length of the comment corpus generated by product reviews, resulting in a high repetition rate. Therefore, the effective and accurate recognition of new words is a problem that requires an urgent solution. Hence, in this paper, we propose a method for discovering new words from product comments based on Mutual Information and improved Branch Entropy. Firstly, by calculating the Co-occurrence Frequency and Mutual Information between words and adjacent words, the character strings of words after pre-processing and word segmentation are expanded left and right respectively to discover the potential word set. The candidate set of new words is obtained by means of an improved support filtering algorithm. Finally, a new word set is built by applying an improved Branch Entropy filtering algorithm and removing old words. The experimental results show that this method can accurately and effectively identify new words in product comments. [ABSTRACT FROM AUTHOR]

Accumulation of impurities within cold trap device results in degradation of efficient performance in a nuclear reactor systems. The impurities have to be identified and the device has to be replaced periodically based on the accumulation level. Though there are a few techniques available to identify these impurities from the cold trap device, there are certain limitations in these techniques. In order to overcome these constraints, a new harmless and easy approach for identifying and separating the impurities using the radiographic images of cold traps is proposed in this paper. It includes a new segmentation algorithm to segregate the deposited impurities from the images of the cold trap device after sodium experiment. The experiment begins with thresholding and segmenting of impurities by using transformations onto the radiographic images of the cold trap device. The procedure is experimented on two section of the cold trap's image and it yields far better results on comparison with other segmentation techniques. The results are verified and approved by the scientists working with Fast Breeder Test Reactors. Implementation of the proposed algorithm with the simulation results are presented in this paper. [ABSTRACT FROM AUTHOR]

Multilevel inverters are a new class of dc-ac converters designed for high-power medium voltage and power applications as they work at high switching frequencies and in renewable applications by avoiding stresses like dv/dt and has low harmonic distortion in their output voltage. In variable speed drives and power generation systems, the use of multilevel inverters is obligatory. To estimate the switching positions in inverter configuration with low harmonic distortion value, a fast sequential optimization algorithm has been established. For harmonic reduction in multilevel inverter design, a hybrid optimization technique combining Firefly and the Genetic algorithm was used. In several real-time systems and for solving complex engineering problems, optimization approaches are gaining popularity. Based on Grey Wolf Optimization (GWO), this research paper proposes a novel multilevel inverter architecture. The GWO algorithm is based on the natural leadership hierarchy and hunting mechanism of grey wolves (Canis lupus). The Grey Wolf Optimizer (GWO) algorithm is used to find the best switching angles for a cascaded multilevel inverter in order to eliminate some high order harmonics while sustaining the desired fundamental voltage. The proposed inverter has 15 stages, and the circuit’s unique feature includes a limited switching device. This proposed method comprises, MOSFET-based switches well as three DC sources in the main circuit. The switching parameters of the inverter topology are tuned using GWO in this methodology. The THD value of the proposed system is reduced to 6.629% compared to that of Multilevel Inverter using Genetic Algorithm, standalone power supply, Firefly assisted Genetic Algorithm, hybrid APSO algorithm, DC voltage regulation and FPGA. A few simulation studies have been included in this paper to affirm the capability of the hybrid topologies with various condition parameters, dynamic changes, and modulation indexes. [ABSTRACT FROM AUTHOR]

A malicious URL is a link that is created to spread spams, phishing, malware, ransomware, spyware, etc. A user may download malware that can adversely affect the computer by clicking on an infected URL, or might be convinced to provide confidential information to a fraudulent website causing serious losses. These threats must be identified and handled in a decent time and in an effective way. Detection is traditionally done through the blacklist usage method, which relies on keyword matching with previously known malicious domain names stored in a repository. This method is fast and easy to implement, with the advantage of having low false-positive rates regarding previously recognized malicious URLs. However, this method cannot recognize newly created malicious URLs. To solve this problem, many machine-learning models have been used. In this paper, we introduce an effective machine learning approach that uses an ensemble learner algorithm called AdaBoost (Adaptive Boosting), combined with different algorithms that enhance detection. For datasets filtration, we used CfsSubsetEval technique, which is an algorithm that searches for a subset of features that work well together. Datasets were collected from the UNB repository; divided into four categories: spam, phishing, malware, and defacement URLs; combined with benign URLs, dataset content is based on lexical features. The experimental results indicate that the proposed approach was successful in enhancing the detection accuracy of malicious URLs with less false-positive rates for all experimental algorithms. [ABSTRACT FROM AUTHOR]

In this paper we propose a computerized breast cancer detection and breast masses classification system utilizing mammograms. The motivation of the proposed method is to detect breast cancer tumors in early stages with more accuracy and less negative false cases. Our proposed method utilizes clustering of different features by segmenting the breast mammogram and then extracts deep features using the presented Convolution Neural Network (CNN). The extracted features are then combined with subjective features such as shape, texture and density. The combined features are then utilized by the Extreme Learning Machine Clustering (ELMC) algorithm to combine segments together to identify the breast mass Region of Interest (ROI). We present a detection method utilizing the ELMC clustering technique. Building a multi-feature set, the ELMC classifier is utilized to perform classification of normal, benign and cancer breast masses. Feature fusion is performed on the extracted shape, texture and density features forming a fusion feature set. In the automated detection phase, we utilize the fusion feature sets for classification. Extensive experimentation has been carried out to validate the ability of our proposed method. We utilized a dataset of 600 female mammograms. The experiments measure the accuracy of our proposed detection and classification method. The CNN coupled with the Extreme Learning Machine Clustering algorithm achieves the highest accuracy, sensitivity, specificity and ROC measures when combined with a multi-feature set. The model achieves 98.53% cancer detection accuracy, 95.6% benign detection accuracy and 95% for normal cases. [ABSTRACT FROM AUTHOR]

Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard deviation, energy, contrast, etc., of the cancer-affected area. Finally, an unsupervised diffusion classification (UDC) algorithm is explored to narrow down the affected areas. The proposed lung cancer detection method is implemented on two datasets obtained from standard hospital real-time values. The experiment results achieved superior performance in the detection of lung cancer, which demonstrates that our new model can contribute to the early detection of lung cancer. [ABSTRACT FROM AUTHOR]

This paper offers an H-infinity (H∞) controller-based disturbance rejection along with the utilization of the water wave optimization (WWO) algorithm. H∞ controller is used to synthesize the guaranteed performance of certain applications as well as it provides maximum gain at any situation. The proposed work focuses on the conflicts of continuous stirred-tank reactor (CSTR) such as variation in temperature and product concentration. The elimination of these issues is performed with the help of the WWO algorithm along with the controller operation. In general, the algorithmic framework of WWO algorithm is simple, and easy to implement with a small-size population and only a few control parameters. The planned work gives the enhanced performance by means of disturbance rejection when compared with the PID, ADRC and ANN controllers. Additionally, the proposed work improves the lifespan of the offered application through the elimination disorders. The overall process is implemented in the MATLAB working platform and the results are compared with the preceding methods to show the expected performance. [ABSTRACT FROM AUTHOR]

The dissemination of the COVID-19 viruses now extends the usage of video consultations to share perspectives and virtual medical consultations, save expenses and health procedures, track the success of care proposals with detail, consistency, and ease from moment to time. The research aims to study the security of video consultations. We will also present the advantages and disadvantages of video consultations and the complications of their implementation. This paper mainly proposes a practical, high-efficiency video encoding technique for the new video encoding technique (HEVC) used in video consultations. The technology offered uses the RC5 block encryption algorithm for encrypting the sensitive bits of HEVC with low complexity, short encoding times in real-time implementations, format-compliant HEVC, and constant bitrate. In addition, this paper presents experimental results comparing the proposed HEVC RC5-based selective encryption (SE) algorithm with the other algorithms used in various operating modes using the Advanced Encryption Standard (AES). Also, this paper provides the security analysis of the proposed HEVC RC5-based SE algorithm for video consultation, including; the correlation analysis between the ciphered video and the original one, the histogram of the ciphered video, and the cipher cycle analysis. The security analysis results ensured that the proposed HEVC RC5- based SE algorithm for video consultation is safe and stable. [ABSTRACT FROM AUTHOR]

Long Range Wide Area Network (LoRaWAN) has been developed to meet the requirements for the enormous device-to-device communication of Internet of Things (IoT) networks, which consist of a large number of participating devices spread over large coverage areas with low data rates and low power consumption. It supports communications in both directions, uplink, and downlink directions. However, the downlink communication in the current LoRaWAN raises the bottleneck issue at gateways due to the used gateway selection algorithm. This paper proposes a novel gateway selection algorithm based on the duty cycle time-off values for the existing gateways, Duty Cycle Gateway Selection (DCGS), to direct acknowledgment packets as downlink traffic towards the most suitable gateway. Thus, the proposed system avoids subsequent retransmission of previously sent traffic that leads to excessive traffic overloading the network. The proposed system avoids exhausting a gateway duty cycle with downlink traffic by distributing the downlink traffic among available gateways based on the duty cycle time off. DCGS is evaluated using FloRa and INET frameworks in the well-known network simulator OMNeT++. The result shows the superior performance of the proposed approach over the existing Signal-to-Noise ratio (SNR) based selection mechanism. It clearly indicates that the DCGS maintains a better confirmed packet delivery rate while reducing number of retransmissions, collisions, and power consumption. [ABSTRACT FROM AUTHOR]

Speech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered response power-phase transform (SRP-PHAT) weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position information. Since theGRUnetwork has the advantage of processing time series data with faster training speed and fewer training parameters, the GRUmodel is adopted to process the separation features of several sequential frames in the same sub-band to estimate the ideal RatioMasking (IRM). The proposed algorithm decomposes themixture signals into time-frequency (TF) units using gammatone filter bank in the frequency domain, and the target speech is reconstructed in the frequency domain bymasking themixture signal according to the estimated IRM. The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost. Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech separation in noisy and reverberant environments, provides good performance in terms of speech quality and intelligibility, and has the generalization capacity to reverberate. [ABSTRACT FROM AUTHOR]