A Transaction Security Accountability Protocol for Electronic Health Systems
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Chian Techapanupreeda Faculty of Engineering and Technology, Mahanakorn University of Technology, Thailand This email address is being protected from spambots. You need JavaScript enabled to view it. |
Ekarat Rattagan Graduate School of Applied Statistics, National Institute of Development Administration, Thailand This email address is being protected from spambots. You need JavaScript enabled to view it. |
Werasak Kurutach Faculty of Engineering and Technology, Mahanakorn University of Technology, Thailand This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: In the last two decades, the term “electronic health (e-health) systems” were extensively mentioned in the healthcare industry with the aim of replacing paper usage and increasing productivity. Unfortunately, these systems are not still widely used by healthcare professionals and patients due to the concerns on security and accountability issues. In this article, we propose an accountability transaction protocol to overcome all security issues for implementing electronic health systems. To validate our proposed protocol, we used both Automated Validation of Internet Security Protocols and Applications (AVISPA) and Scyther as the tools to prove its soundness.
Keywords: Accountability, transaction security, electronic health records, personal health records, cryptography, scyther tool, AVISPA.
Received March 14, 2020; accepted September 12, 2021
SAFRank: Multi-Agent based Approach for Internet Services Selection
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Imran Mujaddid Rabbani Department of Computer Science, University of Engineering and Technology, Pakistan This email address is being protected from spambots. You need JavaScript enabled to view it. |
Muhammad Aslam Department of Computer Science, University of Engineering and Technology, Pakistan This email address is being protected from spambots. You need JavaScript enabled to view it. |
Ana Maria Martinez-Enriquez Department of Computer Science, CINVESTAV-IPN, Mexico This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: In the era of modern world, organization are preferring to adopt smart solutions for their business tasks and managing huge and complex transactions. These solutions are provided through online application infrastructures of Internet of Things (IoT), cloud, fog, and edge computing. In the presence of numerous prospects, the selection benchmark for such offers becomes vibrant, especially, when there is no supportive platform available. Prevailing approaches provide services by evaluating the quality of service parameters, K-Nearest Neighbours (KNN) classifications, k-mean clustering, assigning scores, trustworthiness and fuzzy logic techniques on customer's feedback. However, these approaches classically depend on seeker’ feedback and do ‘not consider interrelationship between the services. Secondly, these techniques do not follow standards derived by well-known organizations like National Institute of Standards and Technology (NIST), International Organization for Standards (ISO), and IEEE. Feedback may be self-generated or biased and leading to inappropriate recommendation to end users. To resolve the issue, we propose multi agent based approach using service association factor that computes interrelationship values among services appearing together in a package as SAFRank and evaluates it on standards along with dynamically defined quality of service parameters. It assists seekers to select the best services on their preferences from pool of IoT and internet services. The technique is tested on leading cloud vendors and results show that it meets the desires of service seekers in all service models in an efficient manner.
Keywords: Internet services, service selection, service association factor, IoT services.
Received April 10, 2020; accepted September 16, 2021
Towards Personalized User Training for Secure Use of Information Systems
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Damjan Fujs Faculty of Computer and Information Science, University of Ljubljana, Slovenia This email address is being protected from spambots. You need JavaScript enabled to view it. |
Simon Vrhovec Faculty of Criminal Justice and Security, University of Maribor, Slovenia This email address is being protected from spambots. You need JavaScript enabled to view it. |
Damjan Vavpotič Faculty of Computer and Information Science, University of Ljubljana, Slovenia This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Information Systems (IS) represent an integral part of our lives, both in the organizational and personal sphere. To use them securely, users must be properly trained. The main problem is that most training processes still use the one-size-fits-all approach where users receive the same kind of learning material. In addition, personalized training may be a more suitable approach however a comprehensive process for IS user profiling and personalized IS user training improvement has not been introduced yet. This paper proposes a novel approach for personalized user training for secure use of IS to fill in this gap. The proposed approach focuses on three key dimensions (i.e., the personalization process, selection of training tools and materials, and participants) and is composed of five phases covering the identification of key IS security elements, IS user profiling and personalization of IS security training. It is scalable to all company sizes and aims to lower both the IS training costs and optimization of outcomes. As a side-effect, it also helps to lower user resistance to participation in IS security training.
Keywords: Education, training, awareness, adaptation, tailoring, information security, cost-benefit.
Received July 27, 2020; accepted October 10, 2021
A Heuristic Tool for Measuring Software Quality Using Program Language Standards
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Mohammad Abdallah Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Jordan This email address is being protected from spambots. You need JavaScript enabled to view it. |
Mustafa Alrifaee Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Jordan This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Quality is a critical aspect of any software system. Indeed, it is a key factor for the competitiveness, longevity, and effectiveness of software products. Code review facilitates the discovery of programming errors and defects, and using programming language standards is such a technique. In this study, we developed a code review technique for achieving maximum software quality by using programming language standards. A Java Code Quality Reviewer tool (JCQR) was proposed as a practical technique. It is an automated Java code reviewer that uses SUN and other customized Java standards. The JCQR tool produces new quality-measurement information that indicates applied, satisfied, and violated rules in a piece of code. It also suggests whether code quality should be improved. Accordingly, it can aid junior developers and students in establishing a successful programming attitude. JCQR uses customized SUN-based Java programming language standards. Therefore, it fails to cover certain features of Java.
Keywords: Java, code review, code inspection, quality.
Received August 28, 2020; accepted July 12, 2021
Machine Learning-Based Model for Prediction of Power Consumption in Smart Grid
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Shamik Tiwari School of Computer Science, University of Petroleum and Energy Studies, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Anurag Jain* School of Computer Science, University of Petroleum and Energy Studies, India This email address is being protected from spambots. You need JavaScript enabled to view it. *corresponding author |
Kusum Yadav Computer Science and Information Systems, University of Hail, Hail This email address is being protected from spambots. You need JavaScript enabled to view it. |
Rabie Ramadan Computer Science and Information Systems, University of Hail, Hail This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: An electric grid consists of transformers, generation centers, communication links, control stations, and distributors. Collectively these components help in moving power from one electricity station to commercial and domestic consumers. Traditional grid stations can’t predict the dynamic need of consumers’ electricity. Furthermore, these traditional grids are not sufficiently strong and adaptable. This is the driving force for the transition towards a smart grid. A modern smart grid is a self-healing, long-lasting electrical system that can adapt to changing client needs. Machine learning has aided in grid stability calculation in the face of dynamically shifting consumer demands. By avoiding a breakdown, the smart grid has been transformed into a reliable smart grid. The authors of this study used a variety of machine learning-based algorithms to estimate grid stability to avoid a breakdown situation. An open-access dataset lying on Kaggle repository has been used for experimental work. Experiments are conducted in a simulation environment generated through Python. Using the Bagging classifier algorithm, the suggested model has attained an accuracy level of 97.9% while predicting the load. A precise prediction of power demand will aid in the avoidance of grid failure, hence improving grid stability and robustness.
Keywords: Smart grid, grid stability, machine learning, load balancing, prediction model.
Received September 18, 2020; accepted August 31, 2021
https://doi.org/10.34028/iajit/19/3/5
Sentiment Analysis System using Hybrid Word Embeddings with Convolutional Recurrent Neural Network
Abstract: There have been wide ranges of innovations in sentiment analysis in recent past, with most effective ones involving use of various word embeddings methods for analysis of sentiments. GloVe and Word2Vec are acclaimed to be two most frequently used. A common problem with simple pre-trained embedding methods is that these ignore information related to sentiments of input texts and further depend on large text corpus for training purpose and generation of relevant vectors which is hindrance to researches involving smaller sized corpuses. The aim of proposed study is to propose a novel methodology for sentiment analysis that uses hybrid embeddings with a target to enhance features of available pre-trained embedding. Proposed hybrid embeddings use Part of Speech (POS) tagging and word2position vector over fastText with varied assortments of attached vectors to the pre-trained embedding vectors. The resultant form of hybrid embeddings is fed to our ensemble network-Convolutional Recurrent Neural Network (CRNN). The methodology has been tested for accuracy via different Ensemble models of deep learning and standard sentiment dataset with accuracy value of 90.21 using Movie Review (MVR) Dataset V2. Results show that proposed methodology is effective for sentiment analysis and is capable of incorporating even more linguistic knowledge-based techniques to further improve results of sentiment analysis.
Keywords: Analysis of sentiments, convolutional neural networks, part of speech tagging, natural language processing, word2Vec, GloVe, fastText, hybrid embedding, recurrent neural networks.
Received January 13, 2021; accepted October 14, 2021
https://doi.org/10.34028/iajit/19/3/6
Secrecy Capacity Analysis of Reconfigurable Intelligent Surface Based Vehicular Networks
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Ashokraj Murugesan Department of Electronics and Communication Engineering, Oxford Engineering College, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Ananthi Govindasamay Department of Electronics and Communication Engineering, Thiagarajar College of Engineering College, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: As Vehicular Networks based technologies are in the close proximity of deployment for various wireless applications under proposal worldwide, this research paper proposes secrecy capacity analysis for Reconfigurable Intelligent Surface (RIS) based Vehicular Network. The proposed network model has a fixed infrastructure comprising of source node, destination node incorporated with single antenna and passive eavesdropper forming the scenario. RIS based Vehicular communication links are modelled by Rayleigh fading for source-to RIS link and RIS to destination Vehicle, whereas Eavesdropper channel links are Double-Rayleigh amplitude distribution, induced by double scattering in the channel. For this scenario, we derive the closed-form expressions for the average Secrecy Capacity and Secrecy Outage Probability (SOP) of the considered system. Though, Secrecy Capacity analysis is an excellent performance metric for assessing eavesdropper based system, it has been reported by various research works, this research paper differentiates from other research papers by considering different secrecy rates and different distances of eavesdropper as presented in simulation. Further, to validate the obtained simulation results, theoretical results are also derived for assessing performance of SOP for various secrecy rates which is the highlight of this research paper and it can be used as benchmark for various research works to proceed further.
Keywords: Channel capacity, rayleigh fading channel, vehicular network, secrecy outage probability.
Received February 8, 2021; accepted October 10, 2021
TempTracker: A Service Oriented Temporal Natural Language Processing Based Tool for Document Data Characterization and Social Network Analysis
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Onur Can Sert Information Technologies, Zalando, Ireland Ltd This email address is being protected from spambots. You need JavaScript enabled to view it. |
Sibel Tariyan Özyer Department of Computer Engineering, Ankara Medipol University, Turkey This email address is being protected from spambots. You need JavaScript enabled to view it. |
Deniz Bestepe Department of Computer Engineering, Istanbul Medipol University, Turkey This email address is being protected from spambots. You need JavaScript enabled to view it. |
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Abstract: With the advent of Web 2.0 based technology, news sites and micro-blog sites have become popular and have attracted the attention of people around the world. Existing textual data captured by these sites is highly beneficial for extracting (a) new information to analyze, and (b) temporal course of change in entities, topics and sentiment for differing granularities. This has been demonstrated by the study described in this paper. After collecting the data, several directions have been investigated in order to demonstrate its effectiveness under the umbrella of entity extraction, topic and sentiment analysis using Natural Language Processing (NLP) tools, temporal social media analysis, and time varying trend results of entity and sentiment aspect of entities. A service-based architecture has been proposed to process text data with NLP tools and to enrich the data. Text data is collected and processed via NLP tools. It is retrieved upon request for data analysis. The reported results illustrate the applicability and effectiveness of the conducted study.
Keywords: Natural language processing, entity recognition, sentiment analysis, social network analysis.
Received August 7, 2021; accepted October 7, 2021
https://doi.org/10.34028/iajit/19/3/8
Improved Semantic Inpainting Architecture Augmented with a Facial Landmark Detector
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Mirza Sami School of Computing, University of Alabama at Birmingham, USA This email address is being protected from spambots. You need JavaScript enabled to view it. |
Israt Naiyer Department of Computer Science and Engineering, Brac University, Bangladesh This email address is being protected from spambots. You need JavaScript enabled to view it. |
Ehsanul Khan Department of Computer Science and Engineering, Brac University, Bangladesh This email address is being protected from spambots. You need JavaScript enabled to view it. |
Jia Uddin AI and Big Data Department, Endicott College, Woosong University, South Korea This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: This paper presents an augmented method for image completion, particularly for images of human faces by leveraging on deep learning based inpainting techniques. Face completion generally tend to be a daunting task because of the relatively low uniformity of a face attributed to structures like eyes, nose, etc. Here, understanding the top level context is paramount for proper semantic completion. The method presented improves upon existing inpainting techniques that reduce context difference by locating the closest encoding of the damaged image in the latent space of a pre-trained deep generator. However, these existing methods fail to consider key facial structures (eyes, nose, jawline, etc.,) and their respective location to each other. This paper mitigates this by introducing a face landmark detector and a corresponding landmark loss. This landmark loss is added to the construction loss between the damaged and generated image and the adversarial loss of the generative model. The model was trained with the celeb A dataset, tools like pyamg, pillow and the OpenCV library was used for image manipulation and facial landmark detection. There are three main weighted parameters that balance the effect of the three loss functions in this paper, namely context loss, landmark loss and prior loss. Experimental results demonstrate that the added landmark loss attributes to better understanding of top-level context and hence the model can generate more visually appealing in painted images than the existing model.The model obtained average Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PNSR) scores of 0.851 and 33.448 for different orientations of the face and 0.896 and 31.473, respectively, for various types masks.
Keywords: Structural image inpainting, generative adversarial networks, facial landmark, synthetic image.
Received December 27, 2019; accepted February 2, 2021
Choosing Decision Tree-Based Boundary Patterns in the Intrusion Detection Systems with Large Data Sets
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Hamidreza Ghaffari Department of Computer Engineering, Islamic Azad University of ferdows, Iran This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Today, due to the growing use of computer networks, the issue of security of these networks and the use of intrusion detection systems has received serious attention. A major challenge in intrusion detection systems is the enormous amount of data. The generalization capability of support vector machine has attracted the attention of intrusion detection systems in the last years. The main drawbacks of a support vector machine occur during its training phase, which is computationally expensive and dependent on the size of the input dataset. In this study, a new algorithm to speed up support vector machine training time is presented. In proposed method, First, Ant Colony Optimization (ACO) is used to find prototype samples, then a number of prototype samples is randomly selected and the approximate boundary is determined using support vector machine. Based on the approximate boundary obtained, boundary samples are determined using decision tree. Using these boundary samples, final model is obtained. To demonstrate the effectiveness of the proposed method, standard publicly available datasets have been used. The experiment results show that despite the data reduction, the proposed model produces results with similar accuracy and in a faster way than state-of-the art and the current Support Vector Machine (SVM) implementations.
Keywords: Intrusion detection systems, boundary patterns, support vector machine, data reduction.
Received February 14, 2020; accepted August 31, 2021
Half-Duplex and Full-Duplex Performance Comparison for Different Fading Channel Using HMR Protocol in MIMO Technology
Abstract: This paper deals with the performance of Half Duplex (HD) and Full Duplex (FD) in different fading channels via relay networks. A new Heterogeneous Multiplex Relay (HMR) protocol is proposed for the achievement of spectrum efficiency over Multiple Input and Multiple Output (MIMO) using HD and FD. The Key idea of the protocol the relay selection among half duplex and full duplex for overcoming fading which achieves diversity and multiplexing gain. The protocol is projected in Massive MIMO with Opportunistic Relay Selection which requires limited feedback/signaling for relay and operation mode selection. The max-min criteria help in finding the best channel which leads to the best relay selection. Consideration of the Coded Cooperation and Successive Interference Cancellation helps recovery of the loss of Multiplexing Gain. Simulation result shows the throughput performance of Heterogeneous Multiplex Relay (HMR) protocol compared to HD and FD. HMR protocol provides 80% capacity performance due to its allotted channels between HD and FD. It also shows the performance of Bit Error Rate (BER) vs Signal to Noise Ratio (SNR) values compared to Rayleigh, Rician, and Nakagami Fading Channels.
Keywords: Relay network, HD, FD, HMR Protocol, MIMO, BER, SNR.
Received October 14, 2020; accepted July 27, 2021
A Novel Technique of Noise Cancellation based on Stationary Bionic Wavelet Transform and WATV: Application for ECG Denoising
Abstract: In this paper, is proposed a novel technique of Electrocardiogram (ECG) denoising. It is based on the application of Wavelet/Total-Variation (WATV) denoising approach in the domain of the Stationary Bionic Wavelet Transform (SBWT). It consists firstly in applying the SBWT to the noisy ECG signal for obtaining two noisy coefficients named wtb1 and wtb2 which are respectively details and approximation coefficients. For estimating the level of noise altering the signal, named σ, we use wtb1. This noise is an additive Gaussian white noise. The thresholding of wtb1 is secondly performed employing the soft thresholding and a denoised coefficient wtd1 is obtained. This thresholding requires the use of a certain threshold, thr which is computed using σ. Thedenoising of wtb2 is performed using WATV denoising method and we obtain a denoised coefficient, wtd2. This WATV denoising method also uses . The denoised ECG signal is finally obtained by applying the inverse of SBWT (SBWT-1) to wtd1 and wtd2. The proposed technique performance is justified by the results obtained from the computations of Signal to Noise Ratio (SNR), Minimum Square Error (MSE), Mean Absolute Error (MAE), Peak-SNR (PSNR) and Cross-Correlation (CC).
Keywords: Convex optimization, denoising, ECG, WATV, stationary bionic wavelet transform.
Received October 24, 2020; accepted August 17, 2021
Research on the Similarity between Nodes with Hypernymy/Hyponymy Relations based on IC and Taxonomical Structure
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Xiaogang Zhang College of Information Engineering, Tarim University, China This email address is being protected from spambots. You need JavaScript enabled to view it. |
Lili Sun Science and Technology Office, Tarim University, China This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: The similarity method has an important effect on some tasks of natural language processing, such as information retrieval, automatic translation and named entity recognition. Hypernymy/hyponymy relations are widespread in semantic webs and knowledge graphs, so computing the similarity of hypernymy/hyponymy is a key issue in the text processing field. All measures of both feature-based and IC-based methods have obvious deficiencies. The feature-based method estimated the similarity by the depth of the node, and the IC-based method computed the similarity by the position of the deepest common parent. The deficiency of the feature-based method and IC-based method is that they include one parameter, so the performance is slightly inaccurate and unstable. To address this deficiency, our paper proposed a hybrid method that computes the similarity of hypernymy/hyponymy by a hybrid parameter (dhype(lch)) that implies two parameters: depth of the node and position of the deepest common parent. Compared with several similarity methods, the proposed method achieved better performance in terms of accuracy rate, Pearson correlation coefficient and artificial fitting effect.
Keywords: Computing similarity, information content, ontology and knowledge graph, WordNet, hypernymy/hyponymy.
Received November 16, 2020; accepted August 31, 2021
AFTM-Agent Based Fault Tolerance Manager in Cloud Environment
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Shivani Jaswal University Institute of Computing Chandigarh University Punjab, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Manisha Malhotra University Institute of Computing Chandigarh University Punjab, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: As the number of cloud users are increasing with times, the probability of failures also increases that takes place in any cloud virtual machine. Failures can occur at any point of time in service delivery. There are numerous techniques for reacting proactively towards these failures. In this framework, a service provider is allocated to the user on the basis of ranking of the service provider. This ranking is done by considering parameters such as trust values (calculated by feedback mechanism), check pointing overheads, availability and throughput. Checkpoints are beneficial in triggering save point so that minimal loss of data takes place if any failure occurs. This paper has also compared the proposed framework with Optimal Checkpoints Interval (OCI) framework which is based on triggering checkpoints on constant rates. Results have proven that Agent based Fault Tolerance Manager (AFTM) has 33% to 50% better efficiency results as compared to OCI framework. The results shown in paper demonstrates how better the check pointing overheads, availability and throughput are handled by using AFTM framework. Also, the overheads were reduced to 50% as compared to OCI framework.
Keywords: Agents, checkpoints, virtualization, fault tolerant agent, overheads.
Received January 6, 2021; accepted April 28, 2021
Hybrid FiST_CNN Approach for Feature Extraction for Vision-Based Indian Sign Language Recognition
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Akansha Tyagi Department of Computer Science and Engineering, Maharishi Markandeshwar(Deemed to be) University, IndiaThis email address is being protected from spambots. You need JavaScript enabled to view it. |
Sandhya Bansal Department of Computer Science and Engineering, Maharishi Markandeshwar,(Deemed to be) University, IndiaThis email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Indian Sign Language (ISL) is the commonly used language by the deaf-mute community in the Indian continent. Effective feature extraction is essential for the automatic recognition of gestures. This paper aims at developing an efficient feature extraction technique using Features from Fast Accelerated Segment Test (FAST), Scale-Invariant Feature Transformation (SIFT), and Convolution Neural Networks (CNN). FAST with SIFT are used to detect and compute features, respectively. CNN is used for classification with the hybridization of FAST-SIFT features. The system is implemented and tested using the python-based library Keras. The results of the proposed techniques have been tested on 34 gestures of ISL (24 alphabets set and 10 digit sets) and then compared with the CNN and SIFT_CNN, and it is also tested on two publicly available datasets on Jochen Trisech Dataset (JTD) and NUS-II dataset. The proposed study outperformed some existing ISLR works with an accuracy of 97.89%, 95.68%, 94.90% and 95.87% for ISL-alphabets, MNIST, JTD and NUS-II, respectively.
Keywords: Sign language, indian sign language, fast accelerated segment test, scale-invariant feature transformation, convolution neural networks.
Received September 9, 2020; accepted March 10, 2021