This article will present new classes of confidential data management algorithms for the protection and safeguarding of classified data. These algorithms will constitute a solution dedicated to supporting the process of safeguarding and protecting information with various meanings and in different forms. A special type of data is services, understood as information subject to the processes of management

In the last decade, artificial intelligent systems based on neural networks have gradually become primary source for clinical decision support systems (CDSS) and are being used in diverse areas of medical diagnosis, classification, and prediction. An artificial neural network (ANN) consists of a large number of processing units which performs the computation in a parallel and distributed environment

In several fields such as statistics, machine learning, and bioinformatics, categorical variables are frequently represented as one‐hot encoded vectors. For example, given eight distinct values, we map each value to a byte where only a single bit has been set. We are motivated to quickly compute statistics over such encodings. Given a stream of k‐bit words, we seek to compute k distinct sums corresponding

Load balancing among the processing elements (PEs) of distributed stream processing system (DSPS) is a key issue in the presence of data skewness. Existing data partitioning schemes for DSPS suffer from the scalability problem and system in‐efficiency. Non‐key based partitioning strategies raise prohibitively high memory overhead for the stateful operations with a large number of keys and high data

In this article, we present an improved hybrid algorithm based on ant colony optimization and the polycephalum algorithm. First, we use improved the probability selection mechanism in the ant colony algorithm in order to improve the efficiency of next point searching. Second, in each iteration we update the pheromone concentration of the optimal route by using the polycephalum algorithm. We regard

The quadratic assignment problem (QAP) is a well‐known challenging combinatorial optimization problem that has received many researchers' attention with varied real‐world and industrial applications areas. It is noteworthy to mention that a plethora of nature‐inspired optimization algorithms have successfully been used to solve various optimization problems, including several variants of the QAPs.

Nature‐inspired optimization algorithms especially those based on the hunting behaviors of the creatures assume that the hunting operations are performed in a safe environment. However, generally, there are threats in real‐life for the hunter‐animals. This paper focuses on these threat factors and proposes that they can be used to improve the searching abilities of the algorithms. Gray wolf optimization

Computing methodologies with rapid proliferation and need for greater concurrency have evolved over the years from parallel and distributed computing to latest technologies like cloud computing. A paradigm shift in computing methodologies has been witnessed in the form of exponential speed up, optimization of problem solutions, and solvability of few classically unsolvable problems with emergence of

This article aims to enhance the monitoring accuracy of high concurrent network services. As modern network services grow rapidly in data centers, tail latency has become one of the most crucial deciding factors on user experience. Latency measurement and anomaly detection are essential in evaluating service performance. Existing monitoring tools can be divided into two categories according to estimation

Quality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to

Task‐based execution of graph workloads allows various ordered and unordered implementations, with tasks representing dependencies between graph vertices and edges. This work explores graph algorithms in the context of ordered and unordered task‐based implementations, that trade‐off work‐efficiency with parallelism. The monotonicity of convergent graph solutions is the reason behind the trade‐off between

Extracting frequent itemsets from datasets is an important problem in data mining, for which several mining methods including FP‐Growth have been proposed. FP‐Growth is a classical frequent itemset mining method, which generates pattern databases without candidates. Many improvements have been made in the literature due to the high time complexity and memory usage of FP‐Growth. However, most of them

The advent of information and communication technology has revolutionized the application of smart home through which the residents remotely establish superior control over the smart devices. The users and the smart devices interact over an insecure communication channel that may help an illegitimate user to gain access on the data disseminated by the smart devices, resulting in high exploiting attacks

Micro‐macro models provide a powerful tool to study the relationship between microscale mechanisms and emergent macroscopic behavior. However, the detailed microscopic modeling requires tracking and evolving a potentially high‐dimensional configuration space at high computational cost. In this work, we present a novel parallel algorithm for simulating a high‐dimensional micro‐macro model of a gliding

The isolated toughness of a noncomplete graph G is defined as: , where C(G) is the collection of all vertex cutsets of G and i(G − Y) stands for the number of isolated vertices in G − Y. If G is a complete graph, we set . This isolated toughness parameter is closely related to the existence of factors and fractional factors in graphs. These factors and fractional factors are well‐studied within graph

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi‐angle. But, due to the impact of the COVID‐19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore

The recent increase in Android's popularity has resulted in a swamp of attacks faced by the platform. Several researchers have come out with various static malware detection tools using opcodes as features since opcodes provide the details of intrinsic patterns of application raw data. This article presents a new malware detection approach CogramDroid based on opcode ngrams. The approach classifies

Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains electricity consumption data of 1035 days for two classes: normal and fraudulent. In this work, ET detection

Medical imaging plays the vital role in diagnosis of abnormalities. Periodontitis is a common chronic inflammatory disease damaging the soft tissue that untreated can lead to loss of bone, which supports the teeth. The severity of periodontitis is correlated to pocket depth in alveolar area. Even if analyses of the pocket depth can be manually performed, an automatic assistive tool can drastically

Scheduling is an important technique to improve the productivity of workflow applications in a grid system. Serving workflow applications added overhead on the scheduling system since the scheduler should choose the best zero‐dependent job that improves the performance of a grid system. This paper proposes two dependent job scheduling algorithms based on analyzing the effect of categorical and continuous

As a multi‐dimensional complex system, Cyber physical systems (CPS) integrates computing, network, and physical environment. How to effectively fuse the local detection results is the key to improve the sensing performance in CPS. In order to improve the spectrum sensing performance of cognitive radio, a soft fusion‐based cooperative spectrum sensing using particle swarm optimization (PSO) for cognitive

Wireless sensor networks are used to track and regulate physical conditions like temperature and the environment's humidity. Wireless sensor networks with their advanced features are adopted for many real‐time applications. The limited capacity batteries usually power the sensor nodes. But the big challenge of limited battery capacity obstructs remote and inaccessible areas where their use is the most

Online social networks such as Facebook and Twitter have become part of our daily lives. Their influence on business, politics, and society is considerable. Sensitive or unreliable information can adversely affect individuals, organizations, and governments. Due to the effects of the Covid‐19 epidemic, online news is more plentiful and accessible, which raises concerns about its reliability, quality

Condition monitoring and fault diagnosis of main bearings of large‐scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP) with a new framework by utilizing supervisory control and

In recent years, various approaches to visualization have been adopted for decoding the significance implicit in data. Although the effectiveness of these approaches has been evaluated, scant attention has been paid to their efficiency. In this study, an interactive system was designed based on a two‐component visualization system. In the proposed model, resource libraries were installed on a computer

The LMF ISO standard provides a large cover of lexical knowledge using a fine structure. However, like most of the electronic dictionaries, the available normalized LMF dictionaries comprise only basic morpho‐syntactic and semantic knowledge, such as the meanings of lexical entries through the definitions and the associated examples, and sometimes the indication of the synonyms and antonyms. Other

Wireless sensor networks (WSNs) are an interconnection between hundreds to thousands of geographically distributed sensor nodes often called motes. WSNs are deployed in various fields for collecting data; they are used in healthcare monitoring, weather service, civil and military applications, and so on. WSNs are faced with many challenges due to their lack of computational resources, restrained storage

In the present trend, availability of data increases more and more in all the fields in more complex way. It is very difficult for handling in an effective way with best scalability for proper decision making of knowledge extraction. K‐Means algorithm is the most familiar extensive old‐style partitioned and faster clustering algorithm than other clustering methods. But it is very subtle for initial

Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence

In social media platform, many users post messages to express their interests and preferences daily. Because of its fast and easy access abundant people follow news events and there is a possibility for spreading rumor or fake news. This fake news is unverified at the time of posting. Therefore, it is necessary to detect and remove the fake news before it spread widely. Rumors or fake news are created

Recently, a new management and resource delivery model based on self‐organization and self‐management (SOSM) principles for heterogeneous cloud infrastructures was proposed. The novel architecture was designed and implemented for the provision of cloud resources for delivering high performance computing services. The cost of deployment of such framework on large‐scale cloud infrastructures is prohibitive

Stable isotopic landscape modeling has become a promising approach for answering research questions in multiple disciplines. However, its application has been hindered by the difficulty for individual researchers to collect, compile, and integrate environmental and isotopic data over large spatial and temporal scales and to develop and interpret geostatistical models. To address these challenges, we

As an important method to diagnose gastric cancer, gastric pathological sections images (GPSI) are hard and time‐consuming to be recognized even by an experienced doctor. An efficient method was designed to detect gastric cancer in magnified (20×) GPSI using deep learning technology. A novel DenseNet architecture was applied, modified with a multistage attention module (MSA‐DenseNet). To develop this

In the Internet of Things, the CBAC (capability‐based access control) model is proposed to make devices securely accessed. Here, an owner of a device issues a capability token, that is, a set of access rights, to a subject. The subject is allowed to manipulate ROs (resource objects) in the device according to access rights in the capability token. In the CBAC model, there is a problem a subject sbi

In cloud computing, service composition provides an appealing paradigm to compose existing single services into a powerful workflow. The critical issues in service composition is that service providers may lie about the service's cost and QoS for more profits. However, existing studies only address the issue of services' untruthful cost, none of the studies consider both the issue of untruthful cost

Handling missing values in time series data plays a key role in predicting and forecasting, as complete and clean historical data help to achieve higher accuracy. Numerous research works are present in multivariate time series imputation, but imputation in univariate time series data is least considered due to correlated variables unavailability. This article aims to propose an iterative imputation

The system under investigation contains a single refinery, a unique distribution center, and a multiproduct pipeline. The basic aim is to plan the optimal sequence for pumping products to achieve financial benefit and satisfy the customers with on‐time delivery. In this study, some restrictions (such as batch sizing, discharging rate, forbidden sequences, and settling periods) are considered and the

Textile pattern design is a time‐consuming and tedious work. Mihui, our ongoing developing system, employs deep‐learning techniques to automatically generate huge volumes of patterns with the help of human guidance. However, trained as a black box, Mihui cannot provide customized service for each individual designer who shows unique aesthetics preferences. In this article, we introduce the recommendation

Person re‐identification (Re‐ID) aims at identifying the same person across multiple non‐overlapping camera views. A number of existing methods have been presented for this task in a fully‐supervised manner that requires a large amount of training annotations. However, obtaining high quality labels is extremely time consuming and expensive. In this article, we focus on the semi‐supervised person Re‐ID

As core counts in processors increases, it becomes harder to schedule and distribute work in a timely and scalable manner. This article enhances the scalability of parallel loop schedulers by specializing schedulers for fine‐grain loops. We propose a low‐overhead work distribution mechanism for a static scheduler that uses no atomic operations. We integrate our static scheduler with the Intel OpenMP

In recent days, the major threat in the world is forest fire that affects the biodiversity, climate change, and so forth. So detection process is more essential to monitor the forest region. To detect the forest fire, the paper proposes a novel detection technique of support vector machine (SVM)‐Krill herd that can effectively detect the fire region using different kinds of features. Land surface temperature

Due to the variety in available cloud providers along with the frequent changes in strategic objectives of an enterprise, migrating existing software components to the cloud has become a challenging decision in the software maintenance phase. “Financial” and “customer satisfaction” viewpoints are two important strategic objectives of all enterprises that greatly affect the decision about migration

In real‐world, more and more MaOP (MaOPs) have emerged, which pose great challenge for traditional multi‐objective evolutionary algorithms (MOEAs). In this paper, a many‐objective evolutionary computation based on adaptive hypersphere dynamic angle vector dominance (AHDAVD‐MOEA) is proposed. The AHDAVD‐MOEA has three remarkable characteristics: (1) Based on the angle domination, a dynamic angle vector

Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text‐audio‐video consistency‐driven

In an underwater environment, wireless sensor networks (WSNs) have more potential applications such as homeland safety, naval surveillance, and pollution & exploration monitoring. Underwater wireless sensor networks (UWSNs) help in decreasing the rate of causalities and provide efficient ways for monitoring the underwater activities of the other countries. UWSNs can be efficiently used by the navy

As various algorithms and models spring up, object detection‐based deep learning for image analysis has become an established technology in the field of computer vision. Furthermore, object detection for high‐resolution videos derived from ubiquitous surveillance cameras draws many researchers' attention due to its practical significance and the challenge on detecting accuracy and speed. Existing object

It was estimated there would be 72 million users using spreadsheets monthly in 2017 some of which build complex financial, scientific, and mathematical models. Most of these end‐users are not trained IT professionals but domain experts. In the age of multicore computing and ever‐increasing amounts of data, how can end‐users access this powerful, parallel hardware to accelerate spreadsheet computation

Optimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric

Along with the development of connected devices and smart environments, the Internet of Things (IoT) has been receiving attention for years because of the growth in the number of devices. It enables a new breed of services and applications expected to analyze and augment captured data from IoT devices in order to create meaningful information for end‐users. Due to the limitations of computing and storage

Thanks to the great developments of the latest Field Programmable Gate Array (FPGA), the performance bottleneck of Deep Learning hardware accelerators has been converted to computing ability. In this paper, a novel FPGA‐based Convolutional Neural Network (CNN) Accelerator architecture, named the Effective Pipeline Architecture (EPA) is proposed to optimize the resource usage for the implementation

Genetic algorithms are a practical approach for finding near‐optimal solutions for nondeterministic polynomial‐hard problems. In this work we exploit the parallel processing capability of graphics processing units and Nvidia's CUDA programming platform to accelerate the island model genetic algorithm by modifying the evolutionary operations to fit the hardware architecture and have successfully achieved

Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design

Finding definitions associated with an acronym is a constructive task to many applications like web search, information retrieval, natural language processing, ontology mapping, and question answering. A large‐scale manually built acronym‐definition repositories are available online but updating is a difficult task. To address this problem, previous research works used either heuristics or a machine

A novel definition for weighted entropy is proposed to improve clustering performance for small and diverse datasets. First, intra‐class and inter‐class weighted entropies for categorical and numeric conditional attributes are respectively developed using the mathematical definition of entropy. Second, the weighted entropy is used to calculate cluster weights for mixed conditional attributes. A unique

This article aims to study the design mechanism of landscape lamp modeling based on regional cultural characteristics by using intelligent design methods. Big data technology is used to mine regional cultural level factors from four dimensions: regional, cultural, artistic, and functional. The weight values of all these factors are ranked by analytic hierarchy process (AHP) evaluation system. Meanwhile

Vectorized instructions were introduced to improve the performance of applications. However, they come at the cost of an increase in the power consumption. As a consequence, processors are designed to limit their frequency when such instructions are used in order to respect the thermal design power limit. In this paper, we study and compare the impact of thermal design power and Simple Instruction

Support vector machines (SVM) is a widely used machine learning algorithm. With the increasing amount of research data nowadays, understanding how to do efficient training is more important than ever. This article discusses the performance optimizations and benchmarks related to providing high‐performance support for SVM training. In this research, we have focused on a highly scalable gradient descent‐based

Internet of Things (IoT) networks have become part and parcel of the present life. In the present‐day scenario, IoT networks are distributed, and they generate heterogeneous data to be shared, processed and analyzed. But, the prevailing centralized IoT architecture has numerous security vulnerabilities, specifically in data hosting and transfer. Blockchain, a cryptographically distributed tamper‐proof

With the increase in size of supercomputers, also increases the number of abnormal events. CPU overheating is one such event that decreases the system efficiency: when a CPU overheats, it reduces its frequency. This paper presents a machine learning solution to predict such events. The proposed algorithm is based on dynamic time warping for feature extraction and on a machine learning algorithm for

A successful implementation of demand response (DR) always depends on proper policy and their empower technologies. This article proposed an intelligent multiagent system to idealize the residential DR in distributed network. In our model, the primary stakeholders (smart homes and retailers) are demonstrated as a multifunctional intelligent agent. Home agents (HAs) are able to predict and schedule