EAI Endorsed Transactions on Scalable Information Systems

Issue 4, 2022

Articles
Information

Frequency based Digital Image Forgery Detection Through Optimal Threshold Using SOELTP
Appears in:
sis 22(4): 1

Authors:
Vikas Srivastava, Sanjay Kumar Yadav

Abstract:
INTRODUCTION: Image forgery detection is a very challenging task now a day. Latest tools and applications make it easy. Artefact change our thought and perceptions. OBJECTIVES: A forgery detection system is a need of time to detect image forgery. METHODS: We proposed a blind image forgery detecti…INTRODUCTION: Image forgery detection is a very challenging task now a day. Latest tools and applications make it easy. Artefact change our thought and perceptions.
OBJECTIVES: A forgery detection system is a need of time to detect image forgery.
METHODS: We proposed a blind image forgery detection technique. Optimal threshold-based Enhanced Local Ternary Pattern (OELTP) technique implemented on smoothed image. Features are extracted in the form of frequency to implement Discrete Wavelet Transform (DWT) on the chrominance component of the image. Support Vector Machine is used for classification.
RESULTS: The accuracy of the forgery detection on the proposed technique is better than some of the previous states of work.
CONCLUSION: Image forgery detection system performance has been improved by better localization of the forgery. Performance of the global threshold improved by using the latest technique, and reducing the operational complexity.
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The use of telehealth during the coronavirus (COVID-19) pandemic in oral and maxillofacial surgery – A qualitative analysis
Appears in:
sis 22(4): 2

Authors:
Joshua Lee, Joon Soo Park, Kate N. Wang, Boxi Feng, Marc Tennant, Estie Kruger

Abstract:
Introduction: Telehealth usage increased especially in the coronavirus pandemic. Objective: To determine whether oral and maxillofacial surgeons (OMS) believe that telehealth is an adequate substitute for in-person consultations. Methods: OMS were interviewed. These were transcribed and themes an…Introduction: Telehealth usage increased especially in the coronavirus pandemic.
Objective: To determine whether oral and maxillofacial surgeons (OMS) believe that telehealth is an adequate substitute for in-person consultations.
Methods: OMS were interviewed. These were transcribed and themes and subthemes coded. Quotes were selected to create narratives about themes and subthemes and a frequency table generated.
Results: 20 OMS were interviewed. There were 200 positive, 215 negative, 9 neutral and 256 unstated comments. Major themes were diagnosis, accessibility, patient-centred care, technology and finances. 34 sub-themes were identified. OMS were most satisfied with accessibility and most dissatisfied with diagnosis.
Conclusion: OMS had mixed opinions regarding telehealth. While it can improve access, the technology, interventional capacity and diagnostic ability are limited. Face-to-face was preferred. Further studies are required to improve telehealth.
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Residual network based on convolution attention model and feature fusion for dance motion recognition
Appears in:
sis 22(4): 3

Authors:
Dianhuai Shen, Xueying Jiang, Lin Teng

Abstract:
Traditional posture recognition methods have the problems of low accuracy. Therefore, we propose a residual network based on convolution attention model and future fusion for dance motion recognition. Firstly, the fusion features of the relative position, angle and limb length ratio of human body a…Traditional posture recognition methods have the problems of low accuracy. Therefore, we propose a residual network based on convolution attention model and future fusion for dance motion recognition. Firstly, the fusion features of the relative position, angle and limb length ratio of human body are selected by combining the information of bone key points. The shallow features of the original dance image are extracted and compressed by convolution layer and pooling layer. Then it uses the stacked residual to learn deep features, the gradient dispersion and network degradation can be alleviated. The convolutional attention module is used to assign weighted values to the deep degradation features of the dance. Finally, dance motion detection in complex dance scenes can be realized. The dance movement recognition method proposed in this paper can accurately identify dance motion. Compared with other recognition algorithms, this new algorithm has the best recognition accuracy and faster recognition efficiency.
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Multi-objective fuzzy-based adaptive memetic algorithm with hyper-heuristics to solve university course timetabling problem
Appears in:
sis 22(4): 4

Authors:
Abdul Ghaffar, Mian Usman Sattar, Mubbasher Munir, Zarmeen Qureshi

Abstract:
The university course timetabling is an NP-hard (non-deterministic polynomial-time hard) optimization problem to create a course timetable without conflict. It must assign a set of subject classes to a fixed number of timeslots with physical resources, including rooms and teachers. Avoiding hard co…The university course timetabling is an NP-hard (non-deterministic polynomial-time hard) optimization problem to create a course timetable without conflict. It must assign a set of subject classes to a fixed number of timeslots with physical resources, including rooms and teachers. Avoiding hard constraints creates an executable timetable, whereas the removal of different soft constraints creates a satisfactory timetable. The most common way to resolve this problem is through the use of a hybrid genetic algorithm. The multi-objective fuzzy-based adaptive memetic algorithm, a population-based hybrid genetic approach, is proposed by combining genetic algorithm with local search with tabu search and various artificial intelligence techniques. It starts with generating a random population by using the hyper-heuristics and initial repairing method. By using the hill-climbing algorithm, it iteratively generates new offspring from the population by applying fuzzy- based adaptive crossover and mutation operations. If the solution still contains some conflicts, then the tabu search improves it by applying the most appropriate candidate repeatedly. While getting the workable solution, the algorithm tries to maximize multiple objective functions to get manageable solutions with different perspectives. It efficiently allocates all the required resources to subject classes and generates optimal solutions for the datasets provided by the University of Management & Technology, Lahore. It shows 96.29% accuracy in resolving conflicts compare with that of the simple and hybrid genetic algorithms. A web-based dynamic timetable manager visually represents a timetable and also provides options to adjust conflicts manually.
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Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease
Appears in:
sis 22(4): 5

Authors:
Rubina Sarki, Khandakar Ahmed, Hua Wang, Yanchun Zhang, Kate Wang

Abstract:
Prompt examination increases the chances of effective treatment of Diabetic Eye Disease (DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly used for the initial diagnosis of patients with DED or other eye disorders is the screening of retinal fundus images. Ma…Prompt examination increases the chances of effective treatment of Diabetic Eye Disease (DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly used for the initial diagnosis of patients with DED or other eye disorders is the screening of retinal fundus images. Manual detection with these images is, however, labour intensive and time consuming. As deep learning (DL) has recently been demonstrated to provide impressive benefits to clinical practice, researchers have attempted to use DL method to detect retinal eye diseases from retinal fundus photographs. DL techniques in machine learning (ML) have achieved state-of-the-art performance in the binary classification of healthy and diseased retinal fundus images while the classification of multi-class retinal eye diseases remains an open challenge. Multi- class DED is therefore considered in this study seeking to develop an automated classification framework for DED. Detecting multiple DEDs from retinal fundus images is an important research topic with practical consequences. Our proposed model was tested on various retinal fundus images gathered from the publicly available dataset and annotated by an ophthalmologist. This experiment was conducted employing a new convolutional neural network (CNN) model. Our proposed model for multi-class classification achieved a maximum accuracy of 81.33%, sensitivity of 100%, and specificity of 100%.
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Ubiquitous Healthcare System: Architecture, Prototype Design and Experimental Evaluations
Appears in:
sis 22(4): 6

Authors:
Osama Rehman, Asiya M. Al-Busaidi, Sohaib Ahmed, Kamran Ahsan

Abstract:
Seamless and timely monitoring of patients remains an open challenge in current healthcare systems. The need especially arises for patients with chronic diseases and those susceptible to sudden change in their health, such as cardiac patients and elderly people. Hence, there is a need for designing…Seamless and timely monitoring of patients remains an open challenge in current healthcare systems. The need especially arises for patients with chronic diseases and those susceptible to sudden change in their health, such as cardiac patients and elderly people. Hence, there is a need for designing an automated health monitoring system that could seamlessly and efficiently collect patient information. This can largely improve the decisions made by medical professionals, especially in emergency and time-critical cases. This work proposes the design of a ubiquitous healthcare systems, termed as Remote Health Monitoring System (RHMS) that offers flexible and cost-effective solution. RHMS is designed to be wearable, light-weight and comprise various small non-invasive medical sensors. Results show that RHMS has the potential to provide physicians continuous monitor of patients through a centralized observation system without patients being physically present at any medical facility.
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DURASec: Durable Security Blueprints for Web-Applications Empowering Digital India Initiative
Appears in:
sis 22(4): 7

Authors:
Md Tarique Jamal Ansari, Alka Agrawal, Raees Ahmad Khan

Abstract:
Adversaries always eager to take advantage of flaws in emerging healthcare digital solutions. Very few authors discussed durable application security. Therefore there is a need for a durable security mechanism that must be adequately efficient, is reliable, and defend critical data in an emergency …Adversaries always eager to take advantage of flaws in emerging healthcare digital solutions. Very few authors discussed durable application security. Therefore there is a need for a durable security mechanism that must be adequately efficient, is reliable, and defend critical data in an emergency situation. It ensures that the application can be serviced and meet the needs of users over an extended period of time. This paper presents the fuzzy TOPSIS based method to evaluate the behavioural impact for durable security in the context of the Digital India initiative. This paper also presents novel DURASec blueprints for trustworthy and quality healthcare application development.. Even though the advantages of such technologies may outweigh the dangers, hospitals, drugstores, clinics, practitioners, the drug industry as well as medical device manufacturers, should be prepared to identify and minimize security threats in order to protect sensitive healthcare data.
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Bibliometric Mapping of Trends, Applications and Challenges of Artificial Intelligence in Smart Cities
Appears in:
sis 22(4): 8

Authors:
Shilpi Harnal, Gaurav Sharma, Swati Malik, Gagandeep Kaur, Savita Khurana, Prabhjot Kaur, Sarita Simaiya, Deepak Bagga

Abstract:
INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affor…INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affordable cost. Smart Cities are part of the solution to the growing challenges of urbanization. The adoption of new technologies like artificial intelligence (AI) is transforming cities, making them smarter, faster, and predicting opportunities for improvement.
OBJECTIVES: This study is conducting a detailed bibliometric survey to investigate the applications and trends of Artificial Intelligence research for different areas of smart cities and emphasizing the potential effects and challenges of AI adaptation in smart cities over the past 30.5 years.
METHODS: For this study, the Scopus database was used to collect a total of 1925 documents published between 1991-2021 (July). The bibliometric analysis includes document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of most cited documents.
RESULTS: The analyzed findings of this research study reflect not only the significance of AI technology for various applications within numerous sectors in the smart city but also major obstacles in AI research for various sectors of smart cities.
CONCLUSION: The research demonstrates that AI has the ability to construct today’s and tomorrow’s smart cities, but that each region’s potentials, conditions, and circumstances must be addressed in order to achieve a smooth internet city development.
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A novel SURF-RANSAC matching method for athletics posture recognition
Appears in:
sis 22(4): 9

Author:
Fang Duan

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173785. In athletics sports, accurate identification and correction of athlete's wrong posture can improve the quality of athlete's daily training. In the course of athletics spor…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173785.

In athletics sports, accurate identification and correction of athlete's wrong posture can improve the quality of athlete's daily training. In the course of athletics sports, affine deformation of human body is easy to occur, which leads to the appearance of action feature points with low brightness and shading. However, the traditional method is to extract these feature points and compare them with the correct posture to realize the recognition and correction of posture, which leads to the failure of real-time detection and correction of athletes' wrong posture. Therefore, this paper proposes a method of posture recognition and correction for athletes with depth image bone tracking. The threshold method is used to preprocess the image, and the Kalman filter is used to filter the acquired image. The motion feature points are obtained from the filtered image by Gaussian distribution function. By improving SURF-RANSAC method, marginal points and action feature points with low brightness are screened out. Euclidean distance method is used to determine the distance between two adjacent feature points, and feedback monitoring principle is used to identify and correct the wrong posture. The simulation results show that the improved posture recognition and correction method of depth image bone tracking can realize tracking and monitoring of track and field athletes' movements, complete the detection and recognition of track and field sports posture with high accuracy and strong stability.
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Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement
Appears in:
sis 22(4): 10

Authors:
Xi Zhang, Jiyue Wang

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173789. Aiming at the problems of fuzzy details and excessive enhancement in traditional robot infrared image enhancement algorithms, a robot infrared image enhancement method ba…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173789.

Aiming at the problems of fuzzy details and excessive enhancement in traditional robot infrared image enhancement algorithms, a robot infrared image enhancement method based on Retinex theory and contourlet-based non-local mean is proposed. Firstly, the single-scale Retinex method is used to adjust the gray level of the over-dark and over-bright parts of the image. Then, the contourlet-based non-local mean is used to decompose the image to obtain the basic layer and detail layer. Histogram equalization is used to stretch the contrast of the basic layer, and nonlinear function is used to enhance the detail layer. Finally, the results of different levels are fused to obtain the contrast and detail enhanced robot infrared image. The proposed method is used to simulate several groups of robot infrared images in different scenes, and compared with other enhancement methods for subjective and objective analysis. The results show that the proposed method achieves better performance in detail and contrast enhancement of infrared images.
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Feature extraction of dance movement based on deep learning and deformable part model
Appears in:
sis 22(4): 11

Authors:
Shuang Gao, Xiaowei Wang

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173790. In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movemen…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173790.

In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movement feature extraction. Firstly, the number of filters in DPM is increased, and the branch and bound algorithm is combined to improve the accuracy. Secondly, deep neural network model is used to sample points of interest according to human dance movements. The features extracted from the DPM and deep neural network are fused. It achieves a large reduction in the number of model parameters and avoids the network being too deep. Finally, dance movement recognition is performed on the input data through the full connection layer. Experimental results show that the proposed method in this paper can get the recognition result more quickly and accurately on the dance movement data set.
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Basketball posture recognition based on HOG feature extraction and convolutional neural network
Appears in:
sis 22(4): 12

Author:
Jian Gao

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173787. Basketball posture recognition is one of the important research topics in human-computer interaction and physical education, which is of great significance in medical trea…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173787.

Basketball posture recognition is one of the important research topics in human-computer interaction and physical education, which is of great significance in medical treatment, sports, security and other aspects. With the development of machine learning, the application value of basketball pose recognition in physical education is becoming more and more extensive. This paper constructs a novel convolutional neural network model to recognize basketball posture. The model consists of 11 layers. Convolution and pooling operations are carried out for five basketball postures in the sampled data set. By fusing with the features extracted from HOG, finer features can be obtained. Finally, the data set is trained and recognized by entering the full connection layer for classification. The results show that compared with the traditional machine learning methods, the recognition performance of new model is better.
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An automatic scoring method for Chinese-English spoken translation based on attention LSTM
Appears in:
sis 22(4): 13

Author:
Xiaobin Guo

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173786. In this paper, we propose an automatic scoring method for Chinese-English spoken translation based on attention LSTM. We select semantic keywords, sentence drift and spok…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173786.

In this paper, we propose an automatic scoring method for Chinese-English spoken translation based on attention LSTM. We select semantic keywords, sentence drift and spoken fluency as the main parameters of scoring. In order to improve the accuracy of keyword scoring, this paper uses synonym discrimination method to identify the synonyms in the examinees' answer keywords. At the sentence level, attention LSTM model is used to analyze examinees' translation of sentence general idea. Finally, spoken fluency is scored based on tempo/rate and speech distribution. The final translation quality score is obtained by combining the weighted scores of the three parameters. The experimental results show that the proposed method is in good agreement with the result of manual grading, and achieves the expected design goal compared with other methods.
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Non-local clustering via sparse prior for sports image denoising
Appears in:
sis 22(4): 14

Author:
Ying Zhang

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173794. Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image into the denoising process, a non-local clu…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173794.

Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image into the denoising process, a non-local clustering image denoising algorithm is proposed. A sparse representation dictionary is obtained by combining the image blocks of external clean image and internal noise image. The sparse coefficient estimation of ideal image is obtained by global similar block matching. Based on the class dictionary and the estimated sparse coefficient, a sparse reconstruction method based on compressed sensing technology is used to denoise the image. Experimental results show that compared with traditional image denoising methods, the proposed algorithm can significantly reduce the denoising block effect and preserve more details while transitioning more naturally in the flat area of the image.
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Region proposal network based on context information feature fusion for vehicle detection
Appears in:
sis 22(4): 15

Author:
Zengyong Xu

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173795. By using the traditional methods, the feature information extracted from vehicle target detection is insufficient, which leads to the low accuracy in identifying small tar…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173795.

By using the traditional methods, the feature information extracted from vehicle target detection is insufficient, which leads to the low accuracy in identifying small target vehicles or blocked targets. Therefore, we propose a region proposal network (RPN) based on context information feature fusion for vehicle detection. RPN obtains feature vectors of fixed length as vehicle target features. Context information fusion network obtains the corresponding context information features on the feature maps of different layers. Finally, the two features are fused. In addition, in order to solve the problem of data imbalance, experiments on PASCAL VOC2007 and PASCAL VOC2012 data sets with difficult sample training show that the proposed method has significantly improved the mean average accuracy (mAP) compared with other methods.
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Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network
Appears in:
sis 22(4): 16

Author:
Aizhen Liu

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173793. In this paper, we propose a new music emotion recognition method based on long short-term memory and forward neural network. First, Mel Frequency Cepstral Coefficient (MFC…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173793.

In this paper, we propose a new music emotion recognition method based on long short-term memory and forward neural network. First, Mel Frequency Cepstral Coefficient (MFCC) and Residual Phase (RP) are weighted to extract music emotion features, which improves the recognition efficiency of music emotion features. Meanwhile, in order to improve the classification accuracy of music emotion and shorten the training time of the new model, Long short-term Memory network (LSTM) and forward neural network (FNN) are combined. Using LSTM as the feature mapping node of FNN, a new deep learning network (LSTM-FNN) is proposed for music emotion recognition and classification training. Finally, we conduct the experiments on the emotion data set. The results show that the proposed algorithm achieves higher recognition accuracy than other state-of-the-art complex networks.
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An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters
Appears in:
sis 22(4): 17

Authors:
Faiz Ullah, Asif Ali Laghari

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173796. INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improv…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173796.

INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF).
OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc.
METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels.
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A novel dilated convolutional neural network model for road scene segmentation
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sis 22(4): 18

Authors:
Yachao Zhang, Yuxia Yuan

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173784. Road scene understanding is one of the important modules in the field of autonomous driving. It can provide more information about roads and play an important role in bui…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173784.

Road scene understanding is one of the important modules in the field of autonomous driving. It can provide more information about roads and play an important role in building high-precision maps and real-time planning. Among them, semantic segmentation can assign category information to each pixel of image, which is the most commonly used method in automatic driving scene understanding. However, most commonly used semantic segmentation algorithms cannot achieve a good balance between speed and precision. In this paper, a road scene segmentation model based on dilated convolutional neural network is constructed. The model consists of a front-end module and a context module. The front- end module is an improved structure of VGG-16 fused dilated convolution, and the context module is a cascade of dilated convolution layers with different expansion coefficients, which is trained by a two-stage training method. The network proposed in this paper can run in real time and ensure the accuracy to meet the requirements of practical applications, and has been verified and analyzed on Cityscapes data set.
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Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification
Appears in:
sis 22(4): 19

Authors:
Rui Yang, Dahai Li

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173792. Attention mechanism is widely used in fine-grained image classification. Most of the existing methods are to construct an attention weight map for simple weighted processi…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173792.

Attention mechanism is widely used in fine-grained image classification. Most of the existing methods are to construct an attention weight map for simple weighted processing of features, but there are problems of low efficiency and slow convergence. Therefore, this paper proposes a multi-channel attention fusion mechanism based on the deep neural network model which can be trained end-to-end. Firstly, the different regions corresponding to the object are described by the attention diagram. Then the corresponding higher order statistical characteristics are extracted to obtain the corresponding representation. In many standard fine-grained image classification test tasks, the proposed method works best compared with other methods.
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Multi-attention mechanism based on gate recurrent unit for English text classification
Appears in:
sis 22(4): 20

Author:
Haiying Liu

Abstract:
This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173791. Text classification is one of the core tasks in the field of natural language processing. Aiming at the advantages and disadvantages of current deep learning-based English…This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173791.

Text classification is one of the core tasks in the field of natural language processing. Aiming at the advantages and disadvantages of current deep learning-based English text classification methods in long text classification, this paper proposes an English text classification model, which introduces multi-attention mechanism based on gate recurrent unit (GRU) to focus on important parts of English text. Firstly, sentences and documents are encoded according to the hierarchical structure of English documents. Second, it uses the attention mechanism separately at each level. On the basis of the global object vector, the maximum pooling is used to extract the specific object vector of sentence, so that the encoded document vector has more obvious category features and can pay more attention to the most distinctive semantic features of each English text. Finally, documents are classified according to the constructed English document representation. Experimental results on public data sets show that this model has better classification performance for long English texts with hierarchical structure.
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RETRACTED: A novel SURF-RANSAC matching method for athletics posture recognition [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 21

Author:
Fang Duan

Abstract:
We, the Publisher, have retracted the following article: Fang Duan, (2022). A novel SURF-RANSAC matching method for athletics posture recognition. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172781 The authors submitted the article to the Special Issue on “Real-time image in…We, the Publisher, have retracted the following article: Fang Duan, (2022). A novel SURF-RANSAC matching method for athletics posture recognition. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172781
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
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RETRACTED: Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 22

Authors:
Xi Zhang, Jiyue Wang

Abstract:
We, the Publisher, have retracted the following article: Xi Zhang, Jiyue Wang (2022). Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172782 The authors submitted the article to the Special …We, the Publisher, have retracted the following article: Xi Zhang, Jiyue Wang (2022). Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172782
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Feature extraction of dance movement based on deep learning and deformable part model [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 23

Authors:
Shuang Gao, Xiaowei Wang

Abstract:
We, the Publisher, have retracted the following article: Shuang Gao, Xiaowei Wang (2022). Feature extraction of dance movement based on deep learning and deformable part model. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172783 The authors submitted the article to the Specia…We, the Publisher, have retracted the following article: Shuang Gao, Xiaowei Wang (2022). Feature extraction of dance movement based on deep learning and deformable part model. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172783
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Basketball posture recognition based on HOG feature extraction and convolutional neural network [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 24

Author:
Jian Gao

Abstract:
We, the Publisher, have retracted the following article: Jian Gao (2022). Basketball posture recognition based on HOG feature extraction and convolutional neural network. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172784 The authors submitted the article to the Special Issu…We, the Publisher, have retracted the following article: Jian Gao (2022). Basketball posture recognition based on HOG feature extraction and convolutional neural network. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.5-1-2022.172784
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: An automatic scoring method for Chinese-English spoken translation based on attention LSTM [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 25

Author:
Xiaobin Guo

Abstract:
We, the Publisher, have retracted the following article: Xiaobin Guo (2022). An automatic scoring method for Chinese-English spoken translation based on attention LSTM. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.13-1-2022.172818 The authors submitted the article to the Special Issue…We, the Publisher, have retracted the following article: Xiaobin Guo (2022). An automatic scoring method for Chinese-English spoken translation based on attention LSTM. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.13-1-2022.172818
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Non-local clustering via sparse prior for sports image denoising [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 26

Author:
Ying Zhang

Abstract:
We, the Publisher, have retracted the following article: Ying Zhang (2022). Non-local clustering via sparse prior for sports image denoising. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.13-1-2022.172817 The authors submitted the article to the Special Issue on “Real-time image inform…We, the Publisher, have retracted the following article: Ying Zhang (2022). Non-local clustering via sparse prior for sports image denoising. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.13-1-2022.172817
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Region proposal network based on context information feature fusion for vehicle detection [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 27

Author:
Zengyong Xu

Abstract:
We, the Publisher, have retracted the following article: Zengyong Xu (2022). Region proposal network based on context information feature fusion for vehicle detection. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173161 The authors submitted the article to the Special Issue …We, the Publisher, have retracted the following article: Zengyong Xu (2022). Region proposal network based on context information feature fusion for vehicle detection. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173161
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 28

Author:
Aizhen Liu

Abstract:
We, the Publisher, have retracted the following article: Aizhen Liu (2022). Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173162 The authors submitted the article to the Special Issue on “Re…We, the Publisher, have retracted the following article: Aizhen Liu (2022). Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173162
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): e29

Authors:
Faiz Ullah, Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Asif Ali Wagan, Umair Saeed

Abstract:
We, the Publisher, have retracted the following article: Faiz Ullah, Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Asif Ali Wagan, Umair Saeed (2022). An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters [EAI Endorsed Scal Inf Syst. http://dx…We, the Publisher, have retracted the following article: Faiz Ullah, Kamlesh Kumar, Mansoor Ahmed Khuhro, Asif Ali Laghari, Asif Ali Wagan, Umair Saeed (2022). An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters [EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173163
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: A novel dilated convolutional neural network model for road scene segmentation [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 30

Authors:
Yachao Zhang, Yuxia Yuan

Abstract:
We, the Publisher, have retracted the following article: Yachao Zhang, Yuxia Yuan (2022). A novel dilated convolutional neural network model for road scene segmentation. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173164 The authors submitted the article to the Special Issu…We, the Publisher, have retracted the following article: Yachao Zhang, Yuxia Yuan (2022). A novel dilated convolutional neural network model for road scene segmentation. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173164
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”
more »
RETRACTED: Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 31

Authors:
Rui Yang, Dahai Li

Abstract:
We, the Publisher, have retracted the following article: Rui Yang, Dahai Li (2022). Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173165 The authors sub…We, the Publisher, have retracted the following article: Rui Yang, Dahai Li (2022). Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173165
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »
RETRACTED: Multi-attention mechanism based on gate recurrent unit for English text classification [EAI Endorsed Scal Inf Syst (2022), Online First]
Appears in:
sis 22(4): 32

Author:
Haiying Liu

Abstract:
We, the Publisher, have retracted the following article: Haiying Liu (2022). Multi-attention mechanism based on gate recurrent unit for English text classification. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173166 The authors submitted the article to the Special Issue on …We, the Publisher, have retracted the following article: Haiying Liu (2022). Multi-attention mechanism based on gate recurrent unit for English text classification. EAI Endorsed Scal Inf Syst. http://dx.doi.org/10.4108/eai.27-1-2022.173166
The authors submitted the article to the Special Issue on “Real-time image information processing with deep neural networks and data mining technologies”, edited by the Guest Editors Dr Prof. Hang Li (Northeastern University, China) and Dr Prof. Jochen Schiewe, HafenCity Universität Hamburg, Germany.
From our Research Integrity Team, we performed auditing of the editorial process of this Special Issue, and we identified misconduct during the review process. The generated reviews were simple, generalistic, without rigour, and the same for every submission.
Following the COPE guidelines, we decided to RETRACT this article because “Peer review manipulation suspected after publication”.
We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
more »

Scope

EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2020, the journal started to publish five issues per year. Authors are not charged for article submission and processing.

Topics

The scope of the journal includes:

Scalable distributed information systems
Scalable grid information systems
Parallel information processing and systems
Web information searching and retrieval
Data mining
Content delivery networks (CDN)
VLDB
P2P systems
Scalable mobile…

The scope of the journal includes:

Scalable distributed information systems
Scalable grid information systems
Parallel information processing and systems
Web information searching and retrieval
Data mining
Content delivery networks (CDN)
VLDB
P2P systems
Scalable mobile and wireless database systems
Large scale sensor network systems
Index compression methods
Architectures for scalability
Scalable information system applications
Evaluation metrics for scalability
Information security

Web of Science Core Collection
Ei Compendex
DOAJ
CrossRef
[EBSCO Discovery Service](https://www.ebsco.com/products/ebsco-disco…

Special Issues

Call for Papers: Special issue on: Real-time image information processing with deep neural networks and data mining technologies (Manuscript submission deadline: 2022-02-28; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-…

Guest Editor: Dr. Prof. Hang Li (Northeastern University, China) Guest Editor: Dr. Prof. Jochen Schiewe (HafenCity Universität Hamburg, Germany)

Editors-in-Chief
Hua Wang, Victoria University, Australia
Xiaohua Jia, City University of Hong Kong
Editorial board
Manik Sharma, DAV University, India
Ajay Kattepur (Tata Consultancy Services)
Aniello Castiglione (University of Salerno)
Chang Choi (Chosun University)
Cho-…

Editors-in-Chief
Hua Wang, Victoria University, Australia
Xiaohua Jia, City University of Hong Kong
Editorial board
Manik Sharma, DAV University, India
Ajay Kattepur (Tata Consultancy Services)
Aniello Castiglione (University of Salerno)
Chang Choi (Chosun University)
Cho-Li Wang (University of Hong Kong)
Daniel S. Katz (University of Chicago)
Fabrizio Silvestri (ISTI – CNR, Italy)
Hamed Taherdoost (Hamta Business Solution Snd)
Heng Tao Shen (University of Queensland)
Houbing Song (Embry-Riddle Aeronautical University)
José Manuel Machado (University of Minho, Portugal)
Jose Merseguer (Universidad de Zaragoza)
Jie Li (University of Tsukuba)
Lin Yun (Harbin Engineering University)
Phan Cong Vinh (Nguyen Tat Thanh University)
Raj Gururajan (University of Southern Queensland)
Sherman Chow (Chinese University of Hong Kong)
Silva Fábio (University of Minho, Portugal)
Steve Beitzel (Telcordia)
Tzung-Pei Hong (National University of Kaohsiung, Kaohsing City, Taiwan)
Wang-Chien Lee (The Pennsylvania State University)
Weili Wu (The University of Texas at Dallas)
Xueyan Tang (Nanyang Technological University)
Vijayakumar Ponnusamy (SRM University, India)
J Amudhavel (KL University, India)
Yingshu Li (Georgia State University)
Jerry Chun-Wei Lin (Western Norway University of Applied Sciences, Norway)
Karolj Skala (Ruđer Bošković Institute, Croatia)
Xiao-Zhi Gao (University of Eastern Finland, Finland)
Thaier Hayajneh (Fordham University, USA)
Chin-Ling Chen (Chaoyang University of Technology, Taiwan)
Nuno M. Garcia (Faculty of Sciences, University of Lisbon, Portugal)
Arianna D'Ulizia (Consiglio Nazionale delle Ricerche (CNR), Italy)
Robertas Damaševičius (Kaunas University of Technology (KTU), Lithuania)
Hiep Xuan Huynh (Can Tho University, VietNam)
Ji Zhang (University of Southern Queensland, Australia)
Xiaohui Tao (University of Southern Queensland, Australia)
Ye Wang (National University of Defense Technology, China)
Nageswara Rao Moparthi (KL University, India)
Shuai Liu (Hunan Normal University, China)
Prof Xiaoming Fu (Georg-August-University of Goettingen, Germany)
Prof Zhisheng Huang (Vrije University of Amsterdam)
Prof Rose Quan (Northumbria University, UK)
Prof Shi Dong (Zhoukou Normal University, China)
Dr Limei Peng (Kyungpook National University, South Korea)
Prof Hui Ma( Victoria University of Wellington, New Zealand)
Dr. Venkatesan Subramanian (Indian Institute of Information Technology – Allahabad, India)
Dr Pon Harshavardhanan (VIT Bhopal University, India)
Dr. Manish Kumar (The Indian Institute of Information Technology, Allahabad, India)
Muzammil Hussain, University of Management and Technology, Lahore, Pakistan
Michael Bewong, Charles Sturt University, Australia
Shabir Ahmad, Gachon University, Korea
Vu Nguyen, University of Science, Vietnam
Xiaodi Huang, Charles Sturt University, Australia
Jianming Yong, University of Southern Queensland, Australia
Yogeshwar Vijayakumar Navandar; National Institute of Technology, Indian.
Zhengyi Chai, Tiangong University in China, China
Chuanlong Wang, Taiyuan Normal University, China
Chin-Feng Lee, Chaoyang University of Technology, Taiwan
Hsing-Chung Chen (Jack Chen), Asia University, Taiwan
Wen-Yang Lin, National University of Kaohsiung, Taiwan
Chun-Hao Chen, National Kaohsiung University of Science and Technology, Taiwan
Mudasir Mohd, University of Kashmir, India.
BalaAnand Muthu, INTI International University, Malaysia.
Md Rafiqul Islam, Australian Institute of Higher Education, Australia.
Jin Wang, Institute of Applied Physics and Computational Mathematics, China.
Chandu Thota, University of Nicosia, Cyprus.
Haris M. Khalid, University of Dubai, UAE.
Dr. G. Reza Nasiri, Alzahra University, Tehran, Iran.
Siuly Siuly, Victoria University, Australia
Bishnu Prasad Gautam, Kanazawa Gakuin University, Japan
Sivaparthipan C B, Bharathiar University, India
Ting-Chia Hsu, National Taiwan Normal University, Taiwan
Punitha Palanisamy, Tagore IET, India
Lakshmana Kumar R, Tagore IET, India
Weiwei Jiang, Beijing University of Posts and Telecommunications, Taiwan

Journal Blurb

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index

Publisher: EAI
ISSN: 2032-9407
Volume: 9

Published: 2022-08-19

Frequency based Digital Image Forgery Detection Through Optimal Threshold Using SOELTP

The use of telehealth during the coronavirus (COVID-19) pandemic in oral and maxillofacial surgery – A qualitative analysis

Residual network based on convolution attention model and feature fusion for dance motion recognition

Multi-objective fuzzy-based adaptive memetic algorithm with hyper-heuristics to solve university course timetabling problem

Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease

Ubiquitous Healthcare System: Architecture, Prototype Design and Experimental Evaluations

DURASec: Durable Security Blueprints for Web-Applications Empowering Digital India Initiative

Bibliometric Mapping of Trends, Applications and Challenges of Artificial Intelligence in Smart Cities

A novel SURF-RANSAC matching method for athletics posture recognition

Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement

Feature extraction of dance movement based on deep learning and deformable part model

Basketball posture recognition based on HOG feature extraction and convolutional neural network

An automatic scoring method for Chinese-English spoken translation based on attention LSTM

Non-local clustering via sparse prior for sports image denoising

Region proposal network based on context information feature fusion for vehicle detection

Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network

An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters

A novel dilated convolutional neural network model for road scene segmentation

Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification

Multi-attention mechanism based on gate recurrent unit for English text classification

RETRACTED: A novel SURF-RANSAC matching method for athletics posture recognition [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Feature extraction of dance movement based on deep learning and deformable part model [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Basketball posture recognition based on HOG feature extraction and convolutional neural network [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: An automatic scoring method for Chinese-English spoken translation based on attention LSTM [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Non-local clustering via sparse prior for sports image denoising [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Region proposal network based on context information feature fusion for vehicle detection [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Music Emotion Recognition Based on Long Short-Term Memory and Forward Neural Network [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: An Efficient Algorithm for Image De-noising by using Adaptive Modified Decision Based Median Filters [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: A novel dilated convolutional neural network model for road scene segmentation [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification [EAI Endorsed Scal Inf Syst (2022), Online First]

RETRACTED: Multi-attention mechanism based on gate recurrent unit for English text classification [EAI Endorsed Scal Inf Syst (2022), Online First]

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