Training future programming teachers requires an innovative approach. Not only students need to handle the most current trends in technologies and teaching-learning methodologies, but also they must... Show More

Training future programming teachers requires an innovative approach. Not only students need to handle the most current trends in technologies and teaching-learning methodologies, but also they must develop the capacity and criteria to search and select the most adequate to their context. This work analyzes the application of a collaborative Research-Based Learning methodology in the Programming subject of a master's degree in teacher training. The objective was to create a digital learning ecosystem and analyze the impact on the development of programming teaching skills. The results show that students perceive positive effects on the development of teaching skills, generating useful resources. However, teamwork has conditioned the quality of such resources. The digital ecosystem has allowed students to share knowledge with their peers and forthcoming students. Students who already had the generated ecosystem available valued it very positively. Future programming teachers require lifelong learning which can be supported by this living ecosystem.

Set intersection algorithms between sorted lists are important in triangles counting, community detection in graph analysis and in search engines where the intersection is computed between queries... Show More

Set intersection algorithms between sorted lists are important in triangles counting, community detection in graph analysis and in search engines where the intersection is computed between queries and inverted indexes. Many researches use GPU techniques for solving this intersection problem. The majority of these techniques focus on improving the level of parallelism by reducing redundant comparisons and distributing the workload among GPU threads. In this paper, we propose the GPU Test with Jumps (GTWJ) algorithm to compute the intersection between sorted lists using a new data structure. The idea of GTWJ is to group the data, of each sorted list, into a set of sequences. A sequence is identified by a key and is handled by a thread. Intersection is computed between sequences with the same key. This key allows skipping data packets in parallel if the keys do not match. A counter is used to avoid useless tests between cells of sequences with different lengths. Experiments on the data used in this filed show that GTWJ is better in terms of execution time and number of tests.

In wireless sensor networks, MAC protocol is used to achieve efficient, fair and balanced allocation of wireless channel resources and access control of nodes in the network, and to control the... Show More

In wireless sensor networks, MAC protocol is used to achieve efficient, fair and balanced allocation of wireless channel resources and access control of nodes in the network, and to control the communication process of nodes in the network. The energy consumption of wireless sensor nodes is mainly concentrated in the communication unit, including: data transceiver, idle listening, protocol control overhead, etc. A good MAC protocol is beneficial to reduce unnecessary energy consumption and enhance node life time. At the same time, MAC protocol, as the bottom protocol of wireless sensor network, provides a stable and reliable communication foundation for the realization of the upper protocol. Therefore, a large number of scholars at home and abroad have carried out extensive and in-depth research on MAC layer protocol of network. In this paper, the research process and progress of MAC layer protocol in wireless sensor networks are analyzed, and the research progress of MAC layer protocol in multi-radio frequency and multi-channel wireless sensor networks is analyzed.

Agricultural production and operation produce a large amount of data, which hides valuable knowledge. Data mining technology can effectively explore the connection between various factors from the... Show More

Agricultural production and operation produce a large amount of data, which hides valuable knowledge. Data mining technology can effectively explore the connection between various factors from the massive agricultural data. Classification prediction is one of the most valuable agricultural data mining techniques. This paper presents a new algorithm consisting of machine learning algorithms, feature ranking method and instance filter, which aims to enhance the capability of the random forest algorithm and better solve the problem of agricultural multi-class classification. The performance of the new algorithm was tested by using four standard agricultural multi-class datasets, and the experimental results showed that the newly proposed method performed well on all datasets. Among them, substantial rise in classification accuracy is observed for Eucalyptus dataset. Applying random forest algorithm on Eucalyptus dataset results in classification accuracy as 53.4% and after applying the new algorithm (rough set) the classification accuracy significantly increases to 83.7%.

With the increasing adoption of serverless computing, there is a need for a benchmark. The aim of this paper is to present such a benchmark based on performance and usability testing to better... Show More

With the increasing adoption of serverless computing, there is a need for a benchmark. The aim of this paper is to present such a benchmark based on performance and usability testing to better understand serverless services as well as help practitioners to select between two major clouds, namely, Amazon and Azure. Jmeter tool and system usability scale are used to conduct performance and usability testing, respectively. In addition, a replication package is provided to increase the validity and reliability of the results. The main findings revealed that the serverless platforms are different in their architecture. Even though both of them support the same serverless concept, they differ considerably in structure, development, and creations of services. Overall, both the cloud vendors under study provide the same core capabilities one would expect but, there are some differences too. In particular, usability could be improved to extend the market and capture more customers.

Indian Govt has taken broad step and declared lock down to reduce the community-transmission of the novel “Coronavirus”.Many people tried to utilize this period by doing online work and household... Show More

Indian Govt has taken broad step and declared lock down to reduce the community-transmission of the novel “Coronavirus”.Many people tried to utilize this period by doing online work and household work simulateneouly. Many small scale industries,shops ,agencies,school colleges shut their door following Govt rules and regulations to avoid spreading of virus.People working or engaged in these activities or duties became unemployed .As man is a social animal and feels safe and secured in society due to increase in distance from society from office space and due to financial crises , day by day negative thought impacts their mind and they are mental in stability or pressure . In this study, an attempt was made to prioritize the cause of mental pressure faced by common people. Such that precautionary measures can be taken for the public-health such that appropriate steps can be taken to protect their health from the transmission of this virus. By using the “Grey-technique for order of preference by similarity to ideal solution (Grey-TOPSIS)”method .

Statistical outlier detection techniques uses academic performance oriented results to find the truly brilliant as well as the weakest amongst a colony of students. Machine Learning allows further... Show More

Statistical outlier detection techniques uses academic performance oriented results to find the truly brilliant as well as the weakest amongst a colony of students. Machine Learning allows further partitions within the remaining student community, based on both merit and personality. Present work proposes a decision tree model for predicting three more appropriate categories. It utilizes Text Analytic tools to assess student characteristic traits from their textual responses and feedbacks. The cream of the general pool is chosen to belong to a top class comprising the mentor group, provided they can academically assist the weaker of the lot. But all on the top may not be suited for mentor-ship role - textual assessment data delves to reveal character orientations favouring such decisions. The bulk who can manage their own forms the second class. The bottom of the pool benefits with assistance from the mentor group and comprise the third class.

Abstract— Machine learning can be used to provide systems the ability to automatically learn and improve from experiences without being explicitly programmed. It is fundamentally a multidisciplinary... Show More

Abstract— Machine learning can be used to provide systems the ability to automatically learn and improve from experiences without being explicitly programmed. It is fundamentally a multidisciplinary field that draws on results from Artificial intelligence, probability and statistics, information theory and analysis, among other fields that impact the field of Machine Learning. Ensemble methods are techniques that can be used to improve the predictive ability of a Machine Learning model. An ensemble comprises of individually trained classifiers whose predictions are combined when classifying instances. Some of the currently popular ensemble methods include Boosting, Bagging and Stacking. In this paper, we review these methods and demonstrate why ensembles can often perform better than single models. Additionally, some new experiments are presented to demonstrate the computational ability of Stacking approach.

The stock market volume and price are active areas of research. Behind every dollar of investment, the customer will be hoping for profit in one or the other way. There is a positive correlation... Show More

The stock market volume and price are active areas of research. Behind every dollar of investment, the customer will be hoping for profit in one or the other way. There is a positive correlation between investor sentiment and stock volume. Predicting the stock market is the most difficult task due to the dynamic fluctuation of volume and price. The traditional analysis methods carried out lead to satisfactory results. In this paper, the proposed system uses real-time data from Twitter to detect the user opinion about the product along with the stock volume for prediction. The stock volume data and the Twitter data are collected first, and then the classification of the polarity is carried out using the SentiWordnet dictionary. The algorithm for the prediction of the stock prices uses long short-term memory, a neural network, as the prices are sequentially evolving in nature. The results of the proposed system are correlated between the stock market and Twitter data to obtain better insights that are positive.

With the technological advancements and its reach Social media has become an essential part of our daily lives. Using social media platforms allows propagandist to spread the propaganda more... Show More

With the technological advancements and its reach Social media has become an essential part of our daily lives. Using social media platforms allows propagandist to spread the propaganda more effortlessly and faster than ever before. Machine learning and Natural language processing applications to solve the problem of propaganda in social media has invited researchers attention in recent years. Several techniques and tools have been proposed to counter propagation of propaganda over social media. This work pursues to analyse the trends in research studies in the recent past which address this issue. Our purpose is to conduct a comprehensive literature review of studies focusing on this area. We perform meta-analysis, categorization, and classification of several existing scholarly articles to increase the understanding of the state-of-the-art in the mentioned field.

Aeronautical information service (AIS) involves manifold correlations among aeronautical events. The data mining technology has been used to extract the characteristics of aeronautical information.... Show More

Aeronautical information service (AIS) involves manifold correlations among aeronautical events. The data mining technology has been used to extract the characteristics of aeronautical information. With the aeronautical dynamic information of the notice to airmen (NOTAM) as the study case, this paper carries out semantic analysis on NOTAMs, and establishes a spatio-temporal resource description framework (RDF) schema model by combining a three-tuple RDF model and semantic analysis to extract features of aeronautical information. The new model is constructed by Protégé and NOTAM texts are employed to verify the model. Experiments showed that our proposed model could effectively match the samples of NOTAM information and extract the characteristic data from the NOTAM information. The study is expected to provide a basis for further aeronautical information mining based on knowledge graph.

The extant literature has significantly contributed towards the body of knowledge on wearable technology by exploring its potential for enhancing the well-being of consumers. Another strand of... Show More

The extant literature has significantly contributed towards the body of knowledge on wearable technology by exploring its potential for enhancing the well-being of consumers. Another strand of literature focuses on the challenges being faced by women in the wearables industry. This study has critically investigated the prevailing status of women in the gendered imbalance technology industry particularly the wearables industry. To elucidate this, the study has followed the qualitative approach by conducting an inductive-thematic analysis on the interviews of women achievers in the wearables industry. The findings revealed that the future of wearable technology seems bright in the following sectors: AR/VR and Artificial intelligence (AI) industries, Medical, sports and fitness sectors, Clothing and Jewellery industry, Military, and other industrial applications. However, it would be more promising if women employees and entrepreneurs are encouraged at all levels, and their roles are substantiated in the organizations by reducing challenges and appreciating them for their achievements.

Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. In... Show More

Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. In this paper, an extensive study and review of the existing deep learning (DL)-based techniques used for the purpose of semantic segmentation is carried out along with a summary of the datasets and evaluation metrics used for the same. The paper begins with a general and broader focus on semantic segmentation as a problem and further narrows its focus on existing DL-based approaches for this task. In addition to this, a summary of the traditional methods used for semantic segmentation is also presented towards the beginning. Since the problem of scene understanding is being vastly explored in the computer vision community, especially with the help of semantic segmentation, the authors believe that this paper will benefit active researchers in reviewing and studying the existing state-of-the-art as well as advanced methods for the same.

Beall’s list heavily used as a base for selection of predatory journals by large no. of research studies was ceased from internet in 2017. Thus, status of journal declared as predatory in list... Show More

Beall’s list heavily used as a base for selection of predatory journals by large no. of research studies was ceased from internet in 2017. Thus, status of journal declared as predatory in list is debatable. To verify quality of journals in terms of accuracy and standard of peer review, a sample of Medical Science journals from Beall list and indexed in reputed indexing/abstracting databases was taken. sample of journals was put to quality and credibility check by submitting a deliberately flawed research article. deliberate errors exceed an acceptable norm in submitted research paper. It is astonishing to see that majority of journals (61.96%) accept flawed article on such a sensitive issue, i.e., COVID-19 without peer review and desired revisions. Instant mails reporting paper's acceptance, preceded by multiple emails requesting for submission for Article processing fee, were received frequently. It is found that such publishing ventures are a scare story that only wants to generate as much revenue as possible.

Our study aims to analyze the change in coverage of health issues awareness, printed on the front page of Indian E-Papers (The Hindustan Times and The Times of India) for the pre-and- peri... Show More

Our study aims to analyze the change in coverage of health issues awareness, printed on the front page of Indian E-Papers (The Hindustan Times and The Times of India) for the pre-and- peri coronavirus period. The collected news articles are examined by performing the Latent Dirichlet Allocation algorithm. The sentiment analysis is performed to analyze the change in the emotions aroused from news articles. The outcome regarding the pre-coronavirus period reveals that the focus of the e-papers was mostly on politics, crime, and economy whereas, in the peri-coronavirus period, the e-papers are focusing more (i.e. 40 % topics) on publishing the news related to disseminating the awareness about the Coronavirus disease. The priority of news topics includes the active number of cases, medical facilities, COVID-19 testing. The outcome regarding sentiment analysis reveals that negative sentiments are prominent in the peri-coronavirus period due to fear of the outbreak of the virus.

Traditional agriculture is facing numerous serious issues such as climate variation, population rise, water scarcity, soil degradation, and food security and many more. Though, Aquaponics is a... Show More

Traditional agriculture is facing numerous serious issues such as climate variation, population rise, water scarcity, soil degradation, and food security and many more. Though, Aquaponics is a promising solution, research on building an economically feasible smart Aquaponics system is still a challenge. In this paper, a sustainable smart Aquaponics system using Internet of Things (IOT) and Data Analytics is proposed. The acquired data from sensors such as Ph sensor, and temperature sensor, is analyzed using machine learning techniques to interpret the health of the system. Further, the proposed system includes automated fish feeder which is controlled by Raspberry Pi to automate and reduce the maintenance issues. The android application helps the user to remotely control and monitor the health of the system and also track the critical system parameters. Further the system is driven by the solar power to make it sustainable. A comprehensive survey on the key aspects of Aquaponics including comparison of the proposed model with the traditional aquaponics model is also presented.

This study investigated the utility of the Internet of Things in the public sector and the factors influencing the satisfaction of its users. The study followed two directions, the first... Show More

This study investigated the utility of the Internet of Things in the public sector and the factors influencing the satisfaction of its users. The study followed two directions, the first investigated managers’ perceptions and their satisfaction with using sensors for tracking vehicles. The second direction investigated drivers’ satisfaction with the system used. Results collected from 20 interviews conducted with managers revealed that cost reduction and more control over drivers’ behaviors are the contributions expected from the system. They reported the dissatisfaction of drivers based on violation of their privacy, inequity of implementation, and the low awareness of its utility. Surveys collected from drivers supported the role of trust and privacy, but failed to support the role of usefulness. The qualitative and quantitative nature of this research revealed valuable insights and concluded to important recommendations and future work.

Allotment of nurses to patients is a critical task in terms of better treatment. Nurses should be appointed according to a patient's health condition, type of disease, and financial condition.... Show More

Allotment of nurses to patients is a critical task in terms of better treatment. Nurses should be appointed according to a patient's health condition, type of disease, and financial condition. Again, understaffing of nurses may hamper patient health and condition. Similarly, overstaffing of nurses is a waste of manpower. Adequate staffing of nurses is crucial. The authors propose a technique using game theory to meet overstaffing and understaffing of nurses. Game theory plays a vital role to meet the exact requirement. Nash equilibrium can be used for taking all possible decisions, like appointment of nurses in different categories for smooth treatment of patients. However, the final and most suitable decision can be taken using perfect Nash equilibrium. This technique is a perfect technique to implement in case of vital and critical decision points.

Within the advanced computation time, real-time application pulled in much more attention. Implementing a better high-quality real-time system requires to improve the responsiveness of the tasks... Show More

Within the advanced computation time, real-time application pulled in much more attention. Implementing a better high-quality real-time system requires to improve the responsiveness of the tasks set. This research work aims to achieve the best quality of service (QoS) in terms of improving the responsiveness of aperiodic tasks and also improved acceptability domain, by accepting to execute multiple aperiodic functions while maintaining the feasibility of periodic tasks in a real-time system.The functional analysis with simulation shows that the proposed algorithm is highly effective in terms of task sets deemed schedulable and also by allowing aperiodic tasks that were rejected by existing approaches. The simulation results indicate that it reduces overall average response time of aperiodic tasks approximately 13% at lowest periodic load (35%), 7% at 60% periodic load, and 4% at 80% periodic load, and in all observed circumstances, the proposed novel algorithm received 7%-10% improvement over the existing one.

Often, nonlinearity exists in the financial markets while Artificial Neural Network (ANN) could be used to expect equity market returns for the next years. ANN has been improved its ability to... Show More

Often, nonlinearity exists in the financial markets while Artificial Neural Network (ANN) could be used to expect equity market returns for the next years. ANN has been improved its ability to forecast the daily stock exchange rate and to investigate several feeds using the back propagation algorithm. The proposed research utilized five neural network models, Elman network, Multilayer Perceptron (MLP) network, Elman network with Self-Optimizing Map (SOM), MLP with SOM filter and simple linear regression, for estimating new values. Results were examined to investigate the predicting ability and to provide an effective feeds for future values. The result of the proposed simulation showed that SOM could greatly improve the convergence of the neuron networks; whereas Elman network did a better performance to capture the temporal pattern of the symbolic streams generated by SOM.A benchmark of linear regression model was also employed to show the ability of neural network models to generate higher accuracy in forecasting financial market index.

In today's world, around 230 million people used the Bengali or Bangla language to communicate. These individuals are progressively associated with online exercises on famous micro-blogging and... Show More

In today's world, around 230 million people used the Bengali or Bangla language to communicate. These individuals are progressively associated with online exercises on famous micro-blogging and long-range interpersonal communication locales, imparting insights what's more, musings, and also the vast majority of articles are in the Bengali language. Thus, Bengali people express their emotions using the Bangla language by reviewing, commenting, or recommendations. Sentiment analysis helps determine the people's emotions expressed on social media or several online platforms. Therefore, this study focused on extracting their emotion from a Bengali text by utilizing Word2vector, Skip-Gram, and Continuous Bag of Words (CBOW) with a new Word to Index model by focusing on three individual classes happy, angry, and excited. The authors achieved the highest accuracy of 75% by utilizing the skip-gram model to classify those three types of emotions. This study also outperformed other existing works with LSTM, CNN model with existing datasets.

The emergence of the new economic model of Internet credit industry brings convenience to people's lives, and it also impacts the business model of traditional commercial banking to a great extent.... Show More

The emergence of the new economic model of Internet credit industry brings convenience to people's lives, and it also impacts the business model of traditional commercial banking to a great extent. How to better improve the operation mode, correctly assess and avoid the risks of Internet finance, and create a healthy, orderly, safe and sustainable development environment of Internet finance industry is an important research topic in this industry under the current situation. This paper studies the application of innovative risk early warning model based on big data technology in Internet credit financial risk assessment, aiming at maximizing the utilization efficiency of internal and external data, building a timely, accurate and effective early warning system with independent characteristics, and creating a sharp weapon for intelligent risk early warning. In order to promote the healthy and benign development of China's Internet finance industry.

Prior studies have found that mobile cloud computing could bring substantial cost savings to firms, ultimately resulting in reduced transaction cost to customers. Despite this, financial firms in... Show More

Prior studies have found that mobile cloud computing could bring substantial cost savings to firms, ultimately resulting in reduced transaction cost to customers. Despite this, financial firms in Fiji are slow adopters of mobile cloud computing. The study identifies the challenges faced by financial firms in the adoption of mobile cloud computing to advance the literature on innovation adoption with evidence from a unique context – a Pacific island country. The context is important as the issues are likely to be similar in other developing and remote island countries but the extant research is largely confined to developed countries. Our findings suggest that the lack of mobile cloud computing policy, infrastructure constraints, and security constraints, among others are the main barriers to the adoption thereof. The study contributes by presenting a revised model based on factors that emerged from the study.

AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline... Show More

AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.

Recently, many methods have appeared to solve the problem of the evolution of alignment under the change of ontologies. The main challenge for them is to maintain consistency of alignment after... Show More

Recently, many methods have appeared to solve the problem of the evolution of alignment under the change of ontologies. The main challenge for them is to maintain consistency of alignment after applying the change. An alignment is consistent if and only if the ontologies remain consistent even when used in conjunction with the alignment. The objective of this work is to take a step forward by considering the alignment evolution according to the conservativity principle under the change of ontologies. In this context, an alignment is conservative if the ontological change should not introduce new semantic relationships between concepts from one of the input ontologies. The authors give methods for the conservativity violation detection and repair under the change of ontologies and they carry out an experiment on a dataset adapted from the Ontology Alignment Evaluation Initiative. The experiment demonstrates both the practical applicability of the proposed approach and shows the limits of the alignment evolution methods compared to the alignment conservativity under the change of ontologies.

Credit scoring, aiming to distinguish potential loan defaulter, has played an important role in the financial industry. To further improve the accuracy and efficiency of classification, this paper... Show More

Credit scoring, aiming to distinguish potential loan defaulter, has played an important role in the financial industry. To further improve the accuracy and efficiency of classification, this paper develops an ensemble model combined extreme gradient boosting (XGBoost) and deep neural network (DNN). In the method, training set is divided into different subsets by bagging sampling at first. Then, each subset is trained as a feature extractor by DNN and the extracted features is taken as the input of XGBoost to construct the base classifier. At last, the prediction result is the average of outputs of different base classifiers. In the training verification process, three credit datasets from the UCI machine learning repository are used to evaluate the proposed model. The outcome shows that this model is superior with a significant improvement.

Intelligent manufacturing is an important method for transforming and upgrading enterprise intelligence. Studying the influencing factors of enterprises, intelligent manufacturing can help... Show More

Intelligent manufacturing is an important method for transforming and upgrading enterprise intelligence. Studying the influencing factors of enterprises, intelligent manufacturing can help enterprises formulate more targeted intelligent manufacturing development strategies according to their own stage characteristics to accelerate the intelligent development. The concept of intelligent manufacturing ecosystem is proposed. By exploring the evolution process of intelligent manufacturing ecosystems, a three-stage theoretical model of influencing factors of intelligent manufacturing of enterprises is constructed. The theoretical model and related assumptions are verified using the empirical data of manufacturing enterprises of many provinces and cities in China. The results show that most factors in the digital stage, network stage, and intelligent stage significantly affect the development of enterprise intelligent manufacturing systems. This study provides theoretical reference and suggestions for manufacturing enterprises to develop intelligent manufacturing.

Based on iceberg theory and the questionnaire of competency's elements, hierarchical index system of evaluation of teachers' innovation and entrepreneurship competency in universities is... Show More

Based on iceberg theory and the questionnaire of competency's elements, hierarchical index system of evaluation of teachers' innovation and entrepreneurship competency in universities is established. Through researches, the authors think that analytic hierarchy process (AHP) is a more scientific and reasonable evaluation method whose rationality is checked by satisfactory consistency while the evaluation model of artificial neutral network doesn't consider weighting. If the samples are more than 30, the evaluation of neural network model of teachers' innovation and entrepreneurship competency can achieve the accurate results and satisfactory requirements. Since the method of artificial neutral network has advantages of strong operability, simple rules, and minor errors, it can greatly reduce the workload because it not only eliminates human subjectivity of evaluation and greatly simplifies the process of evaluation, but also improves working efficiency and provides a new way of thinking for evaluation of the teachers' innovation and entrepreneurship competency in universities.

Vehicular ad hoc networks (VANETs) are part of intelligent transportation systems (ITS) and their main objective is to provide communication between vehicles. As self-organizing and configuring... Show More

Vehicular ad hoc networks (VANETs) are part of intelligent transportation systems (ITS) and their main objective is to provide communication between vehicles. As self-organizing and configuring networks, with decentralized control, their performance is totally dependent on the route duration times. This study proposes an analysis of the route duration times in vehicular networks, considering three influential factors: speed, density, and travel orientation. Simulation experiments corroborate that the route duration times increases in denser networks and when vehicles travel in the same direction. However, contrary to common sense, unexpectedly, it is demonstrated that the route duration times in realistic vehicle environments do not decrease as the vehicle speed increases due to the mobility restrictions in this environments (stops at traffic lights and road crossings, braking to avoid collisions, acceleration and deceleration).

Economic losses from earthquakes raised many questions regarding the adequacy of the current seismic design and seismic isolation in data centers. Organizations accommodated new explicit seismic... Show More

Economic losses from earthquakes raised many questions regarding the adequacy of the current seismic design and seismic isolation in data centers. Organizations accommodated new explicit seismic isolation applications in their business continuity and disaster recovery plans. These applications aim acceptable damage levels that correspond to acceptable business interruption for data centers in case of an earthquake. In this study, the authors aim to discuss the importance of seismic isolation applications that can be implemented for data centers within business continuity and disaster recovery planning contexts. To provide a clearer aspect on seismic isolation applications, the topic has been discussed within the framework of international standards. They conclude that GSA, ASCE, and Uptime Institute provide internationally recognized standards which make raised floors a good option for data centers. These standards provide technical documentation for service functioning with high levels of availability during an outage.

The increasing prevalence of business cases utilizing internet of things (IoT) analytics, coupled with the diversity of IoT analytics platforms and their capabilities, poses an immense challenge for... Show More

The increasing prevalence of business cases utilizing internet of things (IoT) analytics, coupled with the diversity of IoT analytics platforms and their capabilities, poses an immense challenge for organizations seeking to make the best choice of IoT analytics platform for their specific use cases. Aiming to characterize the capabilities of IoT analytics, this article presents a reference architecture for IoT analytics platforms created through a qualitative content analysis of online reviews and published implementation architectures of IoT analytics platforms. A further contribution is a taxonomy of the functional and cross-functional capabilities of IoT analytics platforms derived from the analysis of published use cases and related business surveys. Both the reference architecture and the associated taxonomy provide a theoretical basis for further research into IoT analytics capabilities and should therefore facilitate the evaluation, selection, and adoption of IoT analytics solutions through a unified description of their capabilities and functional requirements.

The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search... Show More

The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search engine is the best example of the application of information retrieval to get the most relevant results. In this paper, we propose a new recommendation approach for recommending relevant documents to a search engine’s users. In this work, we proposed a new approach for calculating the similarity between a user query and a list of documents in a search engine. The proposed method uses a new reinforcement learning algorithm based on n-grams model (i.e., a sub-sequence of n constructed elements from a given sequence) and a similarity measure. Results show that our method outperforms some methods from the literature with a high value of accuracy.

Cardiac magnetic resonance imaging is a popular non-invasive technique used for assessing the cardiac performance. Automating the segmentation helps in increased diagnosis accuracy in considerably... Show More

Cardiac magnetic resonance imaging is a popular non-invasive technique used for assessing the cardiac performance. Automating the segmentation helps in increased diagnosis accuracy in considerably less time and effort. In this paper, a novel approach has been proposed to improve the automated segmentation process by increasing the accuracy of segmentation and laying focus on efficient pre-processing of the cardiac magnetic resonance (MR) image. The pre-processing module in the proposed method includes noise estimation and efficient denoising of images using discrete total variation-based non-local means method. Segmentation accuracy is evaluated using measures such as average perpendicular distance and dice similarity coefficient. The performance of all the segmentation techniques is improved. Further segmentation comparison has also been performed using other state-of-the art noise removal techniques for pre-processing, and it was observed that the proposed pre-processing technique outperformed other noise removal techniques in improving the segmentation accuracy.

Smart homes and cities is one of the crucial topics for an individual of any age that requires almost zero computer literacy in order to benefit the leisure and luxury offered by smart homes and... Show More

Smart homes and cities is one of the crucial topics for an individual of any age that requires almost zero computer literacy in order to benefit the leisure and luxury offered by smart homes and cities. Benefits offered by smart homes and cities are not only limited to leisure and luxury but other various areas of an individual's life and to aid them with information and communication, intelligent responses with the information collected and analyzed, environmental protection and public safety with surveillance. Internet of things was invented in 1999. Since then, there has been a huge bloom in technologies, keeping in mind the present systematic development in sensors, wireless technology, artificial intelligence, and machines and devices. This paper outlines the working prototypes that have been developed and deployed in developed countries and recommends to the Pacific Island nations to accept these technologies for the betterment of their countries. It will also compare the usage of energy and cost saving in smart cities and how this can be beneficial to the nations in the Pacific.

This study explores the factors contributing to online users' network centrality in a network on Twitter in the context of a social movement about the “clear the shelters” campaign across the United... Show More

This study explores the factors contributing to online users' network centrality in a network on Twitter in the context of a social movement about the “clear the shelters” campaign across the United States. The authors performed a social network analysis on a network including 13,270 Twitter users and 24,354 relationships to reveal users' betweenness, closeness, eigenvector, in-degree, and out-degree centralities before hypothesis testing. They applied a path analysis including users' centrality measures and their user-related features. The path analysis discovered that the factors of the number of people a user follows, the number of followers a user has, and the number of years since a user had his account increased a user's in-degree connections in the network. Together with the user's out-degree connections along with in-degree links, they pushed a user to have a strategic place in the network. They also implemented a multi-group analysis to find whether the impact of these factors showed differences specifically in replies to, mentions, and retweets networks.

This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The... Show More

This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The analysis of LBP’s two hundred fifty-six patterns has led us to propose a new organization of uniform and no uniform patterns into twenty-eight groups; each group assembled a number of patterns varied according to specific terms. The principal idea is to preserve the low complexity of LBP and simultaneously increase the method robustness against quality degradation caused by image operations like rotation, grey level changes, illumination and mirror effects. The experiments are done with the two texture databases Outex and Brodatz; the tests are proving the robustness of Local Binary Pattern Regrouping (LBPG) under circumstances.

The paper presents an approach to generate and optimize test sequences from the input UML activity diagram. For this, an algorithm is proposed called Unified Modelling Language for Test Sequence... Show More

The paper presents an approach to generate and optimize test sequences from the input UML activity diagram. For this, an algorithm is proposed called Unified Modelling Language for Test Sequence Generation (UMLTSG) that uses a search-based algorithm, named Test Sequence Prioritization using Ant Colony Optimization (TSP ACO) to generate and optimize test sequences. The algorithms overcome the existing limitations of handling complex decision-making activity such as conditional activity, fork activity, and join the activity. The optimization process helps to reduce the number of processing nodes that leads to minimizing the time and cost. The proposed approach experiments on a well-known application Railway Ticket Reservation System (RTRS). APFD metric measures the effectiveness of our approach and found that the prioritized order of test sequences achieved 20% higher APFD score. Apart from this, the authors have also experimented on six real life case studies and obtained an average of 52.16% reduction in redundant test paths.

Big data refers to the enormous heterogeneous data being produced at a brisk pace by a large number of diverse data generating sources. Since traditional data processing technologies are unable to... Show More

Big data refers to the enormous heterogeneous data being produced at a brisk pace by a large number of diverse data generating sources. Since traditional data processing technologies are unable to process big data efficiently, big data is processed using newer distributed storage and processing frameworks. Big data view materialization is a technique to process big data queries efficiently on these distributed frameworks. It generates valuable information, which can be used to take timely decisions, especially in cases of disasters. As there are a very large number of big data views, it is not possible to materialize all of them. Therefore, a subset of big data views needs to be selected for materialization, which optimizes the query response time for a given set of workload queries with minimum overheads. This big data view materialization problem, having objectives minimization of the query evaluation cost of a set of workload queries, while simultaneously minimizing the update processing costs of the materialized views, has been addressed using improved strength pareto evolutionary algorithm (SPEA-2) in this paper. The proposed big data view selection algorithm, which is able to compute a set of diverse non-dominated big data views, is shown to perform better that existing big data view selection algorithms..

Cloud computing has risen as a new computing paradigm providing computing, resources for networking, and storage as a service across the network. Data replication is a phenomenon which brings the... Show More

Cloud computing has risen as a new computing paradigm providing computing, resources for networking, and storage as a service across the network. Data replication is a phenomenon which brings the available and reliable data (e.g., maybe the databases) nearer to the consumers (e.g., cloud applications) to overcome the bottleneck and is becoming a suitable solution. In this paper, the authors study the performance characteristics of a replicated database in cloud computing data centres which improves QoS by reducing communication delays. They formulate a theoretical queueing model of the replicated system by considering the arrival process as Poisson distribution for both types of client request, such as read and write applications. They solve the proposed model with the help of the recursive method, and the relevant performance matrices are derived. The evaluated results from both the mathematical model and extensive simulations help to study the unveil performance and guide the cloud providers for modelling future data replication solutions.

Most of the traditional face modeling methods adopt the parameter-based method to construct, but the face model constructed by this method is too smooth and ignores the detailed features of the... Show More

Most of the traditional face modeling methods adopt the parameter-based method to construct, but the face model constructed by this method is too smooth and ignores the detailed features of the face. To solve this problem, a virtual face modeling method based on 3D motion capture data is proposed in this paper. In order to improve the deformation method for the realistic modeling purpose, this paper divides the modeling process of personalized face into two parts: overall modification and local deformation. The overall deformation modifies the facial shape and the position of the facial features of Maya universal model. Based on the principle of radial basis function interpolation algorithm, a smooth interpolation function is constructed, and the new position coordinates of non-characteristic points are obtained by solving the linear equations, so that they are more in line with the physiological characteristics of human faces. The end result is a more realistic virtual face that you model.

Aiming at the problems existing in the optimal allocation of financial resources, this paper establishes an optimization model and calculates the optimal allocation coefficient. With the help of... Show More

Aiming at the problems existing in the optimal allocation of financial resources, this paper establishes an optimization model and calculates the optimal allocation coefficient. With the help of Markowitz's investment theory, two indicators, which are investment risk and return rate, are analyzed quantitatively. First, by analyzing the allocation efficiency and risk of financial resources, the allocation efficiency model is established, and the problem is decomposed into a finite 0-1 programming problem, which is solved by Hungarian Method. Secondly, considering the minimum allocation risk and the expected maximum return, the multi-objective model is solved by progressive optimal algorithm. The model reflects both unsatisfaction and risk avoidance which are the two characteristics of rational investment behavior. The analysis shows that the model has strong applicability and can be expected to improve the allocation efficiency of financial resources and reduce the allocation risk.

The primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with... Show More

The primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with security and appropriate network selections. Routing algorithms deliberate on the performance of the network to evenly distribute load and thus enhance the lifespan of individual nodes, clustering algorithm decides on allowing the right nodes into the network for enhanced security feature, and finally the ability to analyse, predict the context of individual nodes/sensors in the network. Architecture of the proposed network includes the parameters such as decision making ability to sustain the clusters, decision on members of the clusters until the communication process is completed, local network abilities and disabilities, price, preferences of individuals, terminal and access points of the service providers. Network lifetime of the entire network is observed to be enhanced up to 91% with triple layer architecture.

This study utilized an extended model of the Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the future adoption of accounting information systems... Show More

This study utilized an extended model of the Unified Theory of Acceptance and Use of Technology (UTAUT2) to explore the factors influencing the future adoption of accounting information systems (AIS) by Qatari students. A research model was proposed to predict future adoption, partially moderated by voluntary status of using the system. A sample of 237 students was used to probe their perceptions regarding the use of such systems in their future careers. Students were enrolled in an accounting information systems course in Qatar University. Results indicated that perceived facilitating conditions, performance expectancy and enjoyment were significant predictors of AIS. The other factors failed to be significant predictors. The estimated R2 was 48.4%. The moderation effect of voluntariness was also significant in influencing the relationship between enjoyment and future adoption. The moderator yielded a negative beta, which means that it faded the relationship under consideration. Conclusions and future recommendations are reported at the end of the paper.

This research was conducted to perform an in-depth analysis of the coupling metrics of 10 Open Source Software (OSS) projects obtained from the Comets dataset. More precisely, we analyze the dataset... Show More

This research was conducted to perform an in-depth analysis of the coupling metrics of 10 Open Source Software (OSS) projects obtained from the Comets dataset. More precisely, we analyze the dataset of object-oriented OSS projects (having 17 code related metrics such as coupling, complexity, and size metrics) to (1) examine the relationships among the coupling and other metrics (size, complexity), (2) analyze the pattern in the growth of software metrics, and (3) propose a model for prediction of coupling. To generalize the model of coupling prediction, we have applied different machine learning algorithms and validated their performance on similar datasets. The results indicated that the Random forests algorithm outperforms all other models. The relation analysis specifies the existence of strong positive relationships between the coupling, size, and complexity metrics while the pattern analysis pinpointed the increasing growth trend for coupling. The obtained outcomes will help the developers, project managers, and stakeholders in better understating the state of software health

Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine... Show More

Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.

Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics.... Show More

Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.

Extracting an effective facial feature representation is the critical task for an automatic expression recognition system. Local Binary Pattern (LBP) is known to be a popular texture feature for... Show More

Extracting an effective facial feature representation is the critical task for an automatic expression recognition system. Local Binary Pattern (LBP) is known to be a popular texture feature for facial expression recognition. However, only a few approaches utilize the relationship between local neighborhood pixels itself. This paper presents a Hybrid Local Texture Descriptor (HLTD) which is derived from the logical fusion of Local Neighborhood XNOR Patterns (LNXP) and LBP to investigate the potential of positional pixel relationship in automatic emotion recognition. The LNXP encodes texture information based on two nearest vertical and/or horizontal neighboring pixel of the current pixel whereas LBP encodes the center pixel relationship of the neighboring pixel. After logical feature fusion, the Deep Stacked Autoencoder (DSA) is established on the CK+, MMI and KDEF-dyn dataset and the results show that the proposed HLTD based approach outperforms many of the state of art methods with an average recognition rate of 97.5% for CK+, 94.1% for MMI and 88.5% for KDEF.

Phishing attack is a deceitful attempt to steal the confidential data such as credit card information, and account passwords. In this paper, Phish-Shelter, a novel anti-phishing browser is... Show More

Phishing attack is a deceitful attempt to steal the confidential data such as credit card information, and account passwords. In this paper, Phish-Shelter, a novel anti-phishing browser is developed, which analyzes the URL and the content of phishing page. Phish-Shelter is based on combined supervised machine learning model.Phish-Shelter browser uses two novel feature set, which are used to determine the web page identity. The proposed feature sets include eight features to evaluate the obfuscation-based rule, and eight features to identify search engine. Further, we have taken eleven features which are used to discover contents, and blacklist based rule. Phish-Shelter exploited matching identity features, which determines the degree of similarity of a URL with the blacklisted URLs. Proposed features are independent from third-party services such as web browser history or search engines result. The experimental results indicate that, there is a significant improvement in detection accuracy using proposed features over traditional features.

A novel method for integrating multi-omics data, including gene expression, copy number variation, DNA methylation, and miRNA data, is proposed to identify biomarkers of cancer prognosis. First... Show More

A novel method for integrating multi-omics data, including gene expression, copy number variation, DNA methylation, and miRNA data, is proposed to identify biomarkers of cancer prognosis. First, survival analysis was performed for these four types of omics data to obtain survival-related genes. Next, survival-related genes detected in at least two types of omics data were selected as candidate genes. The four types of omics data only composed of candidate genes were subjected to dimension reduction using an autoencoder to obtain a one-dimensional data representation. The mRMR algorithm was used to screen for key genes. This method was applied to lung squamous cell carcinoma and 20 cancer-related genes were identified. Gene function analysis revealed that the genes were related to cancer. Using survival analysis, the genes were verified to distinguish between high- and low-risk groups. These results indicate that the genes can be used as biomarkers for cancer.

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and... Show More

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and layering was employed to locate the pectoral muscle in mammograms. In most cases, the pectoral muscle is found to be triangular in shape and hence, the ant colony optimization algorithm is employed to accurately estimate the pectoral muscle boundary. The proposed method works with the left mediolateral oblique (MLO) view of mammograms to avoid artifacts. For the right MLO view, the method automatically mirrors the image to the left MLO view. The performance of this method was evaluated using the standard mini MIAS dataset (mammographic image analysis society). The algorithm was tested on 322 images and the overall accuracy of the system was about 97.47 %. The method is robust with respect to the view, shape, size and reduces the processing time. The approach correctly identifies images when the pectoral muscle is completely absent.

User Authentication plays a crucial role in smart card based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly... Show More

User Authentication plays a crucial role in smart card based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly divided into single identity cards and Multi-identity cards. In this paper we have tried to provide a secure Multi-identity Multi-application Smart Card Authentication Scheme. Security is provided to user’s data by using dynamic tokens as verifiers and nested cryptography. A new token is generated after every successful authentication for next iteration. Anonymity is also provided to data servers which provides security against availability attacks. An alternate approach to store data on servers is explored which further enhances the security of the underlying system.

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the... Show More

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

Asymptomatic patients (AP) travel through neighborhoods in communities. The mobility dynamics of the AP makes it hard to tag them with specific interests. The lack of efficient monitoring systems... Show More

Asymptomatic patients (AP) travel through neighborhoods in communities. The mobility dynamics of the AP makes it hard to tag them with specific interests. The lack of efficient monitoring systems can enable the AP to infect several vulnerable people in the communities. This article studied the monitoring of AP through their mobility and trajectory towards reducing the stress of socio-economic complications in the case of pandemics. Mobility and Trajectory based Technique for Monitoring Asymptomatic Patients (MTT-MAP) was established. The time-ordered spatial and temporal trajectory records of the AP were captured through their activities. A grid-based index data structure was designed based on network topology, graph theory and trajectory analysis to cater for the continuous monitoring of the AP over time. Also, concurrent object localisation and recognition, branch and bound, and multi-object instance strategies were adopted. The MTT-MAP has shown efficient when experimented with GeoLife dataset and can be integrated with state-of-the-art patients monitoring systems.

Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and... Show More

Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.

In order to get sparsity clustering ability of unbalanced cloud data set, combined with adaptive environment density screening, data clustering was carried out, and an improved adaptive environment... Show More

In order to get sparsity clustering ability of unbalanced cloud data set, combined with adaptive environment density screening, data clustering was carried out, and an improved adaptive environment density peak clustering algorithm under cloud computing technology was proposed. The storage structure model of grid sparse unbalanced cloud data set is constructed, and structure of grid sparse unbalanced cloud data set is reconstructed by combining feature space reconstruction technology. Rough feature quantity of grid sparse unbalanced cloud data set is extracted, and feature extraction and registration are carried out through strict feature registration method. Cloud fusion and peak feature clustering were carried out according to the grid block distribution of the data set. Peak feature quantities of the grid sparse unbalanced cloud data set were extracted, and binary semantic feature distributed detection of the data was carried out.

Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes... Show More

Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.

Although the traditional convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not... Show More

Although the traditional convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not well captured. With the success of the introduction of self-attentional mechanisms in the field of natural language processing (NLP), people have tried to introduce the attention mechanism in the field of computer vision. It turns out that self-attention can really solve this long-range dependency problem. This paper is a summary on the application of self-attention to image segmentation in the past two years. And think about whether the self-attention module in this field can replace convolution operation in the future.

Different educational programs aim to reduce or control Internet addiction. These are based on a traditional methodology that can be recognized as an exposition or master class (participatory or... Show More

Different educational programs aim to reduce or control Internet addiction. These are based on a traditional methodology that can be recognized as an exposition or master class (participatory or not). In this type of educational programs the student acquires a passive role since they are focused on informing the students of the dangers associated with the development of Internet addiction, but not on the treatment or changes in behavior. If we review these educational programs it can see how they have little effect on students. For this reason, a new educational program approach has been created that fights or limits Internet addiction through a methodological proposal that also focuses on increasing digital competence while limiting or controlling the evolution of Internet addiction. In this article, the design is presented.

This manuscript presents the design of a new approach of human skin color authentication. Skin color is one of the most popular soft biometric modalities. Since a soft biometric modality alone... Show More

This manuscript presents the design of a new approach of human skin color authentication. Skin color is one of the most popular soft biometric modalities. Since a soft biometric modality alone cannot reliably authenticate an individual, this new system is designed to combine skin color results with other pure biometric modalities to increase recognition performance. In the classification process, we first perform facial skin detection by segmentation using the thresholding method in the HSV color space. Then, the K-means algorithm of the clustering method is used to determine the dominant colors on the skin pixels in the RGB model. Variations according to the R, G and B components are recorded in a reference model to enable an individual’s identity to be predicted on the basis of 30 clusters. Experimental results are promising and give a false acceptance rate (FAR) of 29.47% and a false rejection rate (FRR) of 70.53%.

During the teaching of a subject, engineering in this particular case, students acquire knowledge through different learning activities that are guided by the teaching staff. In this process... Show More

During the teaching of a subject, engineering in this particular case, students acquire knowledge through different learning activities that are guided by the teaching staff. In this process, students work with the resources and activities provided by the faculty, acquiring knowledge and demonstrating it through an exam. In this work, students have been asked to share their learning experience and to create knowledge resources that can facilitate learning. Thus, collective knowledge has been created in the subject, which can be used by anyone. The work has shown that students can create useful knowledge for the subject, as well as establish an ontological classification of all the knowledge necessary for learning the subject. The results of this analysis show the ontology defined by the students that is applicable to any subject. This study also describes the process carried out for the creation and management of knowledge by the students themselves, as well as the perception of the use of the collective knowledge created.

This study highlighted the role of consumers' characteristics and their association with social experience in Social Commerce (SC) usage. A theoretical model was developed using Social Exchange and... Show More

This study highlighted the role of consumers' characteristics and their association with social experience in Social Commerce (SC) usage. A theoretical model was developed using Social Exchange and Social Impact theory to disclose the factors influencing user intention in SC by made use of 532 Indonesian respondents. The results showed that the social experience factors were discovered to have enhanced individual personal constructs associated with SC intention, including Habit, Self-efficacy, and Trust. In detail, Perceived Herd and Informational Support were found to have both direct and indirect impacts on user intention through individual personality characteristics. In contrast, Emotional Support has only an indirect effect. Even though prior studies already observe constructs proposed in the theoretical model, the exploration of the extensive association between social experience on the personal element concerning individual intention to use SC has not been adequately addressed. Therefore, it is considered as a contribution of this study toward the body of knowledge.

This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the... Show More

This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the patients of diabetes disease. The results showed that the proposed training algorithm enhanced the performance of ANNs with a better classification accuracy as compared to the other state of art training algorithms for the classification of diabetes on publicly available “Pima Indian Diabetes (PID) dataset”. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data, and their impact on classification accuracy has been discussed. Finally, the results are compared with other nature-inspired algorithms trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO.

With the steady rise in the use of smartphones, specifically android smartphones, there is an ongoing need to build strong Intrusion Detection Systems to protect ourselves from malicious software... Show More

With the steady rise in the use of smartphones, specifically android smartphones, there is an ongoing need to build strong Intrusion Detection Systems to protect ourselves from malicious software attacks, especially on Android smartphones. This work focuses on a sub-group of android malware, scareware. The novelty of this work lies in being able to detect the various scareware families individually using a small number of network attributes, determined by a recursive feature elimination process based on information gain. No work has yet been done on analyzing the scareware families individually. Results of this work show that the number of bytes initially sent back and forth, packet size, amount of time between flows and flow duration are the most important attributes that would be needed to classify a scareware attack. Three classifiers, Decision Tree, Naïve Bayes and OneR, were used for classification. The highest average classification accuracy (79.5%) was achieved by the Decision Tree classifier with a minimum of 44 attributes.

It is necessary to systematically review the literature since the information and communication technology (ICT)-assisted flipped pedagogical approach in English education has been increasingly... Show More

It is necessary to systematically review the literature since the information and communication technology (ICT)-assisted flipped pedagogical approach in English education has been increasingly popular. By way of visualization through CiteSpace and other qualitative research methods, the authors arrived at the conclusion that most of the studies support the ICT-assisted flipped pedagogical approach in EFL education although there are still some different findings. The flipped classroom pedagogy in EFL education may bring many advantages to students, teachers, and researchers. Disadvantages of the ICT-assisted flipped pedagogical approach include difficulty in supervising students' learning activities, organization of in-class academic activities, cautious attitudes, and some negative learning outcomes. They also explored the number of recent publications and citations, co-citation clusters of the cited literature, citation counts, and rankings by bursts, centrality, and sigma. Future research may focus on the interdisciplinary research into the ICT-assisted flipped pedagogical approach.

A number of numerical practices exist that actuaries use to predict annual medical claims expense in an insurance company. This amount needs to be included in the yearly financial budgets.... Show More

A number of numerical practices exist that actuaries use to predict annual medical claims expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This paper presents the development of Artificial Neural Network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models were finished, the focus was to decrease the Mean Absolute Percentage Error by adjusting the parameters such as epoch, learning rate and neuron in different layers. Both Feed Forward and Recurrent Neural Networks were implemented to forecast the yearly claims amount. In conclusion, the Artificial Neural Network Model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims. Recurrent neural network outperformed Feed Forward neural network in terms of accuracy and computation power required to carry out the forecasting.

The structure and composition of the worldwide mobility infrastructure is growing exponentially and urgently needs to reduce the emission of carbon dioxide gas, to decrease the growing of traffic... Show More

The structure and composition of the worldwide mobility infrastructure is growing exponentially and urgently needs to reduce the emission of carbon dioxide gas, to decrease the growing of traffic jam, to limit the over-abundancy traffic signs, and to improve the interoperability of traffic sign between different countries. First, this paper proposes a new mobility paradigm to organize in a global way the mobility based on three arising technology characteristics: high-performance computing efficiency, geo-positioning accuracy and 5G technology. Second, the paper proposes a cluster based approach for managing the mobility of (autonomous-)vehicles in the frame of that new paradigm and propose a set of usage scenarios. Finally, some basic tool are presented in order to implement machine learning based clustering step of the approach.

Instructors often experience difficulties in selecting and sequencing relevant content for deployment into learning management systems. Human error and subjectivity is apparent. This study focuses... Show More

Instructors often experience difficulties in selecting and sequencing relevant content for deployment into learning management systems. Human error and subjectivity is apparent. This study focuses on integrating learning management systems and massive open online courses with the goal of eliminating the human element in uploading content. As far as data sharing is concerned, learning management systems and massive open online courses have known weaknesses. The proposed integration aims to reach a tradeoff between the two systems. A requirements elicitation exercise was conducted towards identification of the component units of a hybrid system. The study utilized a mixed method approach to realize the foundation of the integrated design model. The findings, based on evaluation, showed that experts established completeness of the software design model. They, however, expressed the need for the designs to be extended towards accommodating artificial intelligence features. The proposed designs, thus, present a baseline framework upon which implementation considerations may be built on.

The novel Corona virus SARS-CoV-2 has started with strange new pneumonia of unknown cause in Wuhan city, Hubei province of China. On March 11, 2020, the World Health Organization declared the... Show More

The novel Corona virus SARS-CoV-2 has started with strange new pneumonia of unknown cause in Wuhan city, Hubei province of China. On March 11, 2020, the World Health Organization declared the COVID-19 outbreak as a pandemic. Due to this pandemic situation, the countries all over the world suffered from economic and psychological stress. To analyze the growth of this pandemic, this paper proposes a supervised LSTM model and its variants to predict the infectious cases in India using a publicly available dataset from John Hopkins University. Experimentation has been carried out using various models and window hyper-parameters to predict the infectious rate ahead of a week, 2 weeks, 3 weeks and a month. The prediction results infer that, every individual in India has to be safe at home and to follow the regulations provided by ICMR and the Indian Government to control and prevent others from this complicated epidemic.

This study formulated a model for assessing the risk of coronavirus disease (COVID-19) based on variables associated with the spread of COVID-19 infections. The study used the Mamdani fuzzy logic... Show More

This study formulated a model for assessing the risk of coronavirus disease (COVID-19) based on variables associated with the spread of COVID-19 infections. The study used the Mamdani fuzzy logic model based on a multiple input and single output (MISO) scheme which required 12 inputs and 1 output variable. Each of the input variables was identified using binary values, namely: No and Yes while the spread of COVID-19 was assessed using four nominal linguistic values. Two triangular membership functions were used to formulate each associated variable and four triangular membership functions to formulate the spread of COVID-19 using specific crisp intervals. The results of the study showed that 4096 rules were inferred from the possible combination of the binary linguistic values of the associated variables for the assessment of the spread of COVID-19. The study concluded that knowledge about variables associated with the spread of COVID-19 infection can be adopted for supporting decision-making which affects the assessment of the spread of COVID-19 by stakeholders.

The present papers aims at analyzing the actions of the publishing industry in connection with the transposition of Latin American and Caribbean literature written by women from printed to digital... Show More

The present papers aims at analyzing the actions of the publishing industry in connection with the transposition of Latin American and Caribbean literature written by women from printed to digital format. It presents some results obtained from searches and it detects and analyzes the strategies implemented by the main commercial platforms, digital libraries and bookstores in transposing literary works by women. Likewise, it describes the mechanisms negatively impacting on their representation in the general catalog of Latin American and Caribbean literature. The irruption of works in the public domain and best-selling works by female writers from the region is also discussed.

Internet users are increasing day by day due to its support for many applications and creation of innovative services. Along with this, energy consumption is also becoming an important concern in... Show More

Internet users are increasing day by day due to its support for many applications and creation of innovative services. Along with this, energy consumption is also becoming an important concern in networking. Several researchers have investigated energy saving schemes for networks. Software Defined Networking (SDN) is an excellent choice which improves network functionalities with flexible management aided by centralized control. Recent studies designed efficient algorithms for advancing SDN with overall energy savings. Shutting down idle links and switches are one among numerous such solutions available for SDN. In this paper, we proposed a novel algorithm named AttentiveSDN for reducing the energy consumption in SDNs. Here the controller collects the traffic and the link status from the switches involved in network operation and takes the decision to put which idle links and switches into sleep state . We evaluated the performance of the AttentiveSDN algorithm using Mininet. The result has shown that our proposed approach saves more power than existing solutions.

Software development through teams at different geographical locations is a trend of modern era, which is not only producing good results without costing lot of money but also productive in relation... Show More

Software development through teams at different geographical locations is a trend of modern era, which is not only producing good results without costing lot of money but also productive in relation to its cost, low risk and high return. This shift of perception of working in a group rather than alone is getting stronger day by day and has become an important planning tool and part of their business strategy. In this research classification approaches like SVM and K-NN have been implemented to classify the true positive events of global software development project risk according to Time, Cost and Resource. Comparative analysis has also been performed between these two algorithms to determine the highest accuracy algorithms. Results proved that Support Vector Machine (SVM) performed very well in case of Cost Related Risk and Resource Related Risk. Whereas, KNN is found superior to SVM for Time Related Risk.

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can... Show More

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest X-rays are the common method used to diagnose coronavirus pneumonia and it needs a medical expert to evaluate the result of X-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. In this paper, we present a comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest X-ray & CT open dataset of 5856 images and confusion matrices are used to evaluate model performances.