International Journal of Image, Graphics and Signal Processing最新文献

{"title":"FeatureGAN: Combining GAN and Autoencoder for Pavement Crack Image Data Augmentations","authors":"Xinkai Zhang, Bo Peng, Zaid Al-Huda, Donghai Zhai","doi":"10.5815/ijigsp.2022.05.03","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.05.03","url":null,"abstract":"","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127037253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MLSMBQS: Design of a Machine Learning Based Split & Merge Blockchain Model for QoSAware Secure IoT Deployments","authors":"Shital Agrawal, Shailesh Kumar","doi":"10.5815/ijigsp.2022.05.05","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.05.05","url":null,"abstract":": Internet of Things (IoT) Networks are multitier deployments which assist on-field data to be sensed, processed, communicated, and used for taking control decisions. These deployments utilize hardware-based components for data sensing & actuation, while cloud components are used for data-processing & recommending control decisions. This process involves multiple low-security, low-computational capacity & high-performance entities like IoT Devices, short range communication interfaces, edge devices, routers, & cloud virtual machines. Out of these entities, the IoT Device, router, & short-range communication interfaces are highly vulnerable to a wide-variety of attacks including Distributed Denial of Service (DDoS), worm hole, sybil, Man in the Middle (MiTM), Masquerading, spoofing attacks, etc. To counter these attacks, a wide variety of encryption, key-exchange, and data modification models are proposed by researchers. Each of these models have their own levels of complexities, which reduces QoS of underlying IoT deployments. To overcome this limitation, blockchain-based security models were proposed by researchers, and these models allow for high-speed operations for small-scale networks. But as network size is increased, delay needed for blockchain mining increases exponentially, which limits its applicability. To overcome this issue, a machine learning based blockchain model for QoS-aware secure IoT deployments is proposed in this text. The proposed MLSMBQS model initially deploys a Proof-of-Work (PoW) based blockchain model, and then uses bioinspired computing to split the chain into multiple sub-chains. These sub-chains are termed as shards, and assists in reduction of mining delay via periodic chain splitting process. The significance of this research is use of Elephant Herd Optimization (EHO) which assists in managing number of blockchain-shards via splitting or merging them for different deployment conditions. This decision of splitting or merging depends on blockchain’s security & quality of service (QoS) performance. Due to integration of EHO for creation & management of sidechains, the findings of this research showcase that the proposed model is capable of improving throughput by 8.5%, reduce communication delay by 15.3%, reduce energy consumption by 4.9%, and enhance security performance by 14.8% when compared with existing blockchain & non-blockchain based security models. This is possible because EHO initiates dummy communication requests, which are arbitrarily segregated into malicious & non-malicious, and usedfor continuous QoS & security performance improvement of the proposed model. Due to this continuous performance improvement, the proposed MLSMBQS model is capable of deployment for a wide variety of high-efficiency IoT network scenarios. flexibility","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131610315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformation of Classical to Quantum Image, Representation, Processing and Noise Mitigation","authors":"Shyam R. Sihare","doi":"10.5815/ijigsp.2022.05.02","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.05.02","url":null,"abstract":"","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121221949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sliding Window Based High Utility Item-Sets Mining over Data Stream Using Extended Global Utility Item-Sets Tree","authors":"P. A. Reddy","doi":"10.5815/ijigsp.2022.05.06","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.05.06","url":null,"abstract":"","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121256611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Covid-19 Automatic Detection from CT Images through Transfer Learning","authors":"B. Premamayudu, C. Bhuvaneswari","doi":"10.5815/ijigsp.2022.05.07","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.05.07","url":null,"abstract":"Identification of COVID-19 may help the community and patient to prevent the disease containment and plan to attend disease in right time. Deep neural network models widely used to analyze the medical images of COVID-19 for automatic detection and give the decision support for radiologists to summarize the accurate remarks. This paper proposed deep transfer learning for chest CT scan images to detection and diagnosis of COVID-19. VGG19, InceptionRestNetV3, InceptionV3 and DenseNet201 neural network used for automatic detection of COVID-19 disease form CT scan images (SARS-CoV-2 CT scan Dataset). Four deep transfer learning models were developed, tested and compared. The main objective of this paper is to use pre-trained features and converge pre-trained features with targeted features to improve the classification accuracy. It is observed that DenseNet201 noted the best performance and the classification accuracy is 99.98% for 300 epochs. The findings of the experiments show that the deeper networks struggle to train adequately and give less consistency when there is limited data. The DenseNet201 model adopted for COVID-19 identification from lung CT scans has been intensively optimized with optimal hyper parameters and performs at noteworthy levels with precision 99.2%, recall 100%, specificity 99.2%, and F1 score 99.2%. © 2022, Modern Education and Computer Science Press. All rights reserved.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121297612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review on HEVC Video Forensic Investigation under Compressed Domain","authors":"Neetu Singla, Sushama Nagpal, Jyotsna Singh","doi":"10.5815/ijigsp.2022.05.04","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.05.04","url":null,"abstract":": In recent years, video forensic investigation has become a prominent research area, due to the adverse effect of fake videos on networks, people and society. This paper summarizes all the existing methodologies used for forgery detection in H.265/HEVC videos. HEVC video forgery is generally classified into two categories as video quality forgery and video content forgery. The occurrence of various forgeries such as transcoding, fake-bitrate, inter-frame forgery and intra-frame forgery is deeply analyzed based on features extracted from the HEVC compression domain. The major findings of this research are (i) Less focus on transcoding detection, (ii) Non-availability of HEVC forged video dataset (iii) More focus on double compression detection for forgery detection, and (iv) Non-consideration of adaptive-GOP structure. The forgery detection in the video is critically important due to its wide use as the primary source of information in criminal investigations and proving the authenticity of contents. So, the forgery detection accuracy is of major concern at the present time. Although, various forgery detection methods are developed in past but the findings of this review point out the need of developing more effective detection methods with high accuracy.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124368514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eigen and HOG Features based Algorithm for Human Face Tracking in Different Background Challenging Video Sequences","authors":"Ranganatha S, Y. P. Gowramma","doi":"10.5815/ijigsp.2022.04.06","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.04.06","url":null,"abstract":": We are proposing a unique novel algorithm for tracking human face(s) in different background video sequences. In the beginning, Eigen features and corner points are extracted from the detected face(s). HOG (Histograms of Oriented Gradients) features are isolated from corner points. Eigen and HOG features are combined together. Using these combined features, point tracker keeps track of the face(s) in the frames of the video sequence. Proposed algorithm is being tested on challenging datasets video sequences with technical challenges such as partial occlusion (e.g. moustache, beard, spectacles, helmet, headscarf etc.), changes in expression, variations in illumination and pose; and measured for performance using standard metrics such as accuracy, precision, recall and specificity. Experimental results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117121976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Popular Items Extraction for Covering Reduction Collaborative Filtering","authors":"A. Roko, Umar Muhammad Bello, Abba Almu","doi":"10.5815/ijigsp.2022.04.03","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.04.03","url":null,"abstract":": Recommender Systems are systems that aid users in finding relevant items, products, or services, usually in an online setting. Collaborative Filtering is the most popular approach for building recommender system due to its superior performance. There are several collaborative filtering methods developed, however, all of them have an inherent problem of data sparsity. Covering Reduction Collaborative Filtering (CRCF) is a new collaborative filtering method developed to solve the problem. CRCF has a key feature called popular items extraction algorithm which produces a list of items with the most ratings, however, the algorithm fails in a denser dataset because it allows any item to be in the list. Likewise, the algorithm does not consider the rating values of items while considering the popular items. These make it to produce less accurate recommendation. This research extends CRCF by developing a new popular item extraction algorithm that removes items with low modal ratings and similarly utilizes the rating values in considering the popular items. This newly developed method is incorporated in CRCF and the new method is called Improved Popular Items Extraction for Covering Reduction Collaborative Filtering (ICRCF). Experiment was conducted on Movielens-1M and Movielens-10M datasets using precision, recall and f1-score as performance metrics. The result of the experiment shows that the new method, ICRCF provides a better recommendation than the base method CRCF in all the performance metrics. Furthermore, the new method is able to perform well both at higher and lower levels of sparsity.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124001060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique","authors":"Chandra Shekhar Tiwari, Vijay Kumar Jha","doi":"10.5815/ijigsp.2022.04.02","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.04.02","url":null,"abstract":"To prevent medical data leakage to third parties, algorithm developers have enhanced and modified existing models and tightened the cloud security through complex processes. This research utilizes PlayFair and K-Means clustering algorithm as double-level encryption/ decryption technique with ArnoldCat maps towards securing the medical images in cloud. K-Means is used for segmenting images into pixels and auto-encoders to remove noise (de-noising);the Random Forest regressor, tree-method based ensemble model is used for classification. The study obtained CT scan-images as datasets from ‘Kaggle’ and classifies the images into ‘Non-Covid’ and ‘Covid’ categories. The software utilized is Jupyter-Notebook, in Python. PSNR with MSE evaluation metrics is done using Python. Through testing-and-training datasets, lower MSE score (‘0’) and higher PSNR score (60%) were obtained, stating that, the developed decryption/ encryption model is a good fit that enhances cloud security to preserve digital medical images. © 2022 MECS.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117292496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostics Algorithms for Analysis and Assessment of Steady States and Disorders in Electrical Networks","authors":"Nenad A. Marković, Slobodan N. Bjelić, F. Marković","doi":"10.5815/ijigsp.2022.04.01","DOIUrl":"https://doi.org/10.5815/ijigsp.2022.04.01","url":null,"abstract":": Method of symmetric component is used in analysis of disturbances (short circuits and disturbances) and can be verified by computer simulation and measurement. It is based on possibility of making calculations simple by separating a three-phase asymmetric system into three symmetric systems and three single-phase schemes. It is very important for three-phase electrical networks with linear parameters and the same frequency in the network. The transition of quantities (ems, voltages and currents } , , { I V E F  ) from the asymmetric domain of a three-phase system to the symmetric domain is performed using transformation matrices. Expressions determined in the system of symmetric components are then superimposed on expressions corresponding to conditions of asymmetric system, and superposition is correct if electric quantities are of simple-periodic functions. The paper presents a new method based on analysis using symmetric component methods and diagnostic algorithms for the assessment of the most common disturbances in power grids. The adapted part of the MATLAB package psb.abc,part.mdl was used for method verification, and the obtained results in the form of diagrams and values of diagnostic functions arranged in the form of tables confirm the applicability of the proposed new diagnostic algorithm for analysis and assessment of steady states and disturbances in electrical networks. The proposed diagnostic algorithm enables the realization of the maximum number of diagnostic functions on the basis of which a scheme for diagnosing disorders with classical diode elements or a more modern scheme with microprocessor components can be realized.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123543013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}