International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management最新文献

Identification of sub-networks within a network is essential to understand the functionality of a network. This process is called as 'Community detection'. There are various existing community detection algorithms, and the performance of these algorithms can be varied based on the network structure. In this paper, we introduce a novel random graph generator using a mixture of Gaussian distributions. The community sizes of the generated network depend on the given Gaussian distributions. We then develop simulation studies to understand the impact of density and sparsity of the network on community detection. We use Infomap, Label propagation, Spinglass, and Louvain algorithms to detect communities. The similarity between true communities and detected communities is evaluated using Adjusted Rand Index, Adjusted Mutual Information, and Normalized Mutual Information similarity scores. We also develop a method to generate heatmaps to compare those similarity score values. The results indicate that the Louvain algorithm has the highest capacity to detect perfect communities while Label Propagation has the lowest capacity.

Content-based image retrieval (CBIR) systems are designed to retrieve images that are relevant, based on detailed analysis of latent image characteristics, thus eliminating the dependency of natural language tags, text descriptions, or keywords associated with the images. A CBIR system maintains high-level image visuals in the form of feature vectors, which the retrieval engine leverages for similarity-based matching and ranking for a given query image. In this paper, a CBIR system is proposed for the retrieval of medical images (CBMIR) for enabling the early detection and classification of lung diseases based on lung X-ray images. The proposed CBMIR system is built on the predictive power of deep neural models for the identification and classification of disease-specific features using transfer learning based models trained on standard COVID-19 Chest X-ray image datasets. Experimental evaluation on the standard dataset revealed that the proposed approach achieved an improvement of 49.71% in terms of precision, averaging across various distance metrics. Also, an improvement of 26.55% was observed in the area under precision-recall curve (AUPRC) values across all subclasses.

Deadlock in a shared resource system is a well-known problem. It has been extensively studied and recently a new class of resource reservation technique is researched upon for deadlock free resource management. This class of technique reserves a portion of the resources. The unreserved resources are freely allocated to any process demanding it. When the unreserved resources are not sufficient for a process demand the reserve pool resources are used such that the process completes and releases all the resources it is holding. This paper presents a new resource reservation technique resource driven DFRR. This technique estimates the optimal number of resources needed for a deadlock free resource reservation policy. The correctness is proved in the form of theorem 1. The theorem 2, suggests the resource reservation with minimal resources. The overhead of the resource pool estimation is $O\left(n\right)$ and that of resource management is $O\left(m\right)$ which is optimal for any deadlock handling technique. The effectiveness of the proposed technique is shown in the form of examples and simulation results.

The outbreak of Coronavirus Disease as a pandemic has resulted in a huge saddle on health infrastructure. Preventive measures such as quarantine, social distancing, isolation, and community containment play a pivotal role to contain the spread of exponentially growing COVID cases. This huge burden permitted authorities for institutional/home quarantine for the suspected persons. The biggest challenge for institutional/home quarantine is to monitor and track the movement of quarantined persons. These suspected cases pose a serious threat in outbreak and transmission of the disease. In this paper, an intelligent-Quarantine Monitoring System (iQMS) has been presented which comprises of a wearable IoT-based wristband, bundled with an android mobile app to track and report the absconding quarantined subjects in near real-time. The iQMS incorporates a cloud-based solution with IoT sensors using a global positioning system (GPS) based tracker for geo-fencing breach. The proposed system will facilitate the authorities in remote monitoring and tracking of identified subjects.

A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients' data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients' data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients' data.

Low-rank representation based methods have been used on a variety of medical imaging databases for the segmentation and classification of biomedical images. The subspace segmentation of the data is performed by generating the block diagonal coefficient matrix. Whereas, the data is classified by performing the partitioning of the low-rank representation matrix. There exist several such methods for analysing medical images. The major difference between them lies in the construction of the data dictionary. Most of the time, the input data pattern is used as the dictionary for learning the representation matrix. The direct use of the input data for learning the representation degrades the performance of the model because medical images are subjected to outliers of multiple types, which include environmental lighting, image appearance and varying illumination. These types of errors induce noise in the data. It has been observed that the representation-based model is robust when the training data is clean. If the training data contains corrupted subsamples, the performance of the model drops down. We have addressed the mentioned problem by adopting a class-wise dictionary learning approach. In which the pattern of each class is learnt as the set of tuples in the dictionary. The model has been evaluated on several medical imaging datasets, which includes the Break-his dataset, ALL-IDB, biomedical images, covid CT and chest X-ray. The classification performance of the model is best for the biomedical database (99.16%) followed by the Covid dataset (94%), ALL-IDB database (93.47%) and Break-his dataset (93%).

The telemedicine service concept was mainly established to benefit the underprivileged people from rural areas of a country. However, due to the low literacy and awareness rates among rural population of Bangladesh, the service is not much effective. This paper represents a study on the awareness of the rural population of telemedicine service in Bangladesh and few key findings indicate how the awareness could be increased. The research also suggests that utilizing blockchain technology can enhance the data security and privacy. The research reveals some of the findings which can raise the awareness and popularity of telemedicine service among rural population. We have proposed implementation of blockchain technology which can vastly improve the security issue.

Corona Virus Disease (COVID) 19 has shaken the earth at its root and the devastation has increased the diagnostic burden of radiologists by large. At this crucial juncture, Artificial Intelligence (AI) will go a long way in decreasing the workload of physicians working in the outbreak zone, aiding them to accurately diagnose the new disease. In this work, a hybrid Particle Swarm Optimization-Support Vector Machine based AI algorithm is deployed to analyze the Computed Tomography images automatically providing a high probability in determining the presence of pneumonia due to COVID19. This paper presents a model for training the system to segregate and classify the presence of pneumonia which will in turn save around 50% of the time frame for physicians. This will be especially useful in places of outbreaks where a team of people are working together with the aid of artificial intelligence and/or medical background. The AI incorporated system was distributed in all areas of across the globe. It has been observed that challenges such as data security, testing time effectiveness of model, data discrepancy etc. were positively handled using the deployed system. Moreover, since the AI integrated system identifies the infected patients immediately physicians can confirm the infection and segregate the patients at the right period. A total of 200 training cases have been observed of which 150 were identified to be infected. The proposed work shows specificity of 0.85, a sensitivity of 0.956 and an accuracy of 95.78%.

COVID-19 has changed the way crowded events are organised. Every year thousands of crowded events are organised around the globe. Majority of the crowded events are religious in nature, with sensitivities and emotions attached. Organisation of crowded events, especially during a pandemic like COVID-19, poses a considerable challenge. To contain the spread of a human to human contagious disease, several restrictions, including wearing face masks, maintain social distancing, and adhering to regular cleaning and sanitisation, are critical. These restrictions stress the need for the event organisers, including the local or central government, to overhaul policies and practices about crowd management during a pandemic. Some crowded events are regular, whereas the others are occasional, which could be spontaneous such as a protest march, a political rally or a funeral procession. Controlling spontaneous crowded events can be quite difficult, especially during a crisis like COVID-19 pandemic. In this article, we shall review several crowded events which have taken place during the ongoing pandemic and investigate their impact and contribution in the spreading or containing COVID-19. We shall also provide a framework for effectively organising crowded events during the ongoing and future pandemics.