Recent Advances in Computer Science and Communications - Volume 16, Issue 9, 2023

Blockchain is used to assess health records digitally, preserving the security and immutability of the records. The goal of this study is to make it easier for patients to access their medical records and to send them alert messages about important dates for their check-ups, healthy diet, and appointments. To achieve the above-mentioned objective, an integrated approach using Blockchain and Deep learning is initiated. The first approach is Hyperledger Fabric in Blockchain, i.e., private Blockchain, for storing the data in the medically documented ledger, which can be shared among hospitals as well as Health organizations. The second approach is incorporated with a deep learning algorithm. With the help of algorithms, we can analyse the ledger, after which an alert i.e., consultation, health diet, medication, etc., will be sent to the patient’s registered mobile device. The proposed work uses nine features from the dataset; the features are identification number, age, person gender, disease, weight, consultation date, medication, diagnosis, and diet specification. The study is conducted with several features to give accurate results. The integrated model used in this suggested piece of work automates the patient's alert system for a variety of activities. In terms of precision, recall, and F1 score, testing data demonstrate that the LSTM performs better than the other models. By working together with the calendar software on android mobile devices, alert systems can be improved in the future.

A tumour is an uncontrolled growth of tissues in any part of the body. Tumours are of different types and characteristics and have different treatments. Detection of a tumour in the earlier stages makes the treatment easier. Scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumours. This paper provides a systematic literature survey of techniques for brain tumour segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques. This survey covers publicly available datasets, enhancement techniques, segmentation, feature extraction, and the classification of three different types of brain tumours that include gliomas, meningioma, and pituitary and deep learning algorithms implemented for brain tumour analysis. Finally, this survey provides all the important literature on the detection of brain tumours with their developments.

Background: The present era demands continuous support to bring improvements in executing complex analytics on large-scale data and to work beyond traditional systems. Objective: The need for processing diverse data types and solutions for different domains of the industry is rising. Such needs increase the requirement for sophisticated techniques and methods to enhance the existing platforms and mechanisms further. It provides an opportunity for the research community to investigate further into the existing systems, find potential issues, and propose new ways to improve the current systems. Hadoop is a popular choice to manage and process Big data. It is an open-source platform and a front-runner in the batch processing of large-scale jobs. The economy associated with the cluster in scaling is low as compared to other platforms. However, this popularity by no means guarantees high performance in all scenarios. With the continuous evolution in data development and industrial requirements, it is imperative to investigate and look into new methods and techniques to bring advancements to the existing system. Method: A systematic review is represented in this paper to have an insight into the current progress in this field. Research publications from various sources are taken and analyzed. The performance of a cluster largely depends upon the different job processing mechanisms and policies associated with it. Conclusion: While extensive studies and solutions are proposed, the performance bottlenecks in terms of load balancing, resource utilization, content management, and efficient processing prevail. Not many of the solutions are there on scheduling about the trade-off between different parameters, the process of content splitting and merging is not explored to a large extent and the skew mitigation solutions are more focused on Reduce side of the MapReduce while the Map side is not utilized much for load balancing.

Introduction: Wi-Fi Direct technology enables users to share services in groups, and support Service discovery at the data link layer before creating a P2P Group, and it can be used as a collaborative application integrated into vehicles for multimedia transfer and group configuration between V2X. Compared to cellular networks, Wi-Fi Direct offers a high transmission data rate at a cheaper cost. However, there are numerous hurdles to using Wi-Fi Direct in vehicles, including the fact that Wi-Fi Direct communication has a relatively small coverage area, disconnection may occur multiple times, and the distance between vehicles changes often in a moving setting, which negatively affects the quality of service delivery. Previous studies disregarded the motion and direction of moving objects. Methods: The main contribution of this paper is to use Wi-Fi Direct among vehicles to reduce reliance on the 5G network, thereby addressing the previous challenges. In particular, the main contribution of this paper is to introduce a set of scenarios based on different speeds, directions, and distances between vehicles. The state of the packets is monitored in each scenario to compute the packets delay and loss. We present a new contribution to the services discovery by providing V2V IE with a set of services that reflect the user's interest, such as Web pages, SMS, Audio links, and Video links, using the Generic Advertisement Protocol GAS, and a comparison between the traditional P2P IE and the new V2V IE. Furthermore, the paper introduces a stable Wi-Fi Direct Fuzzy C-Means FCM clustering method based on important parameters impacting the group formation, such as the location, the destination, the direction, the speed of the vehicle, and the user’s Interests List. Results: Based on the results of the FCM, there is still uncertainty in choosing the appropriate time to provide the services to the vehicles. We propose a Type-2 Fuzzy Logic Handover T2FLH system to solve the problem of handling uncertainty about dealing with the available services. Using the simulation on OMNeT++, the proposed scenarios with the fuzzy c-means FCM clustering method are compared to get the best clusters. Then the results were compared with the Type-2 Fuzzy T2FLH system to extract the best scenarios. Conclusion: We concluded from the results of previous experiments that Wi-Fi Direct can be used with vehicles at low speeds and high speeds. In the case of low speeds, it works efficiently depending on OMNET++ results. Therefore, Wi-Fi Direct can be used in vehicle stations and work sites that use limited-speed vehicles such as Clarks machines to alert safety and provide them with information about the devices around them. Bearing in mind that the speed of devices is limited in work areas. In the case of high speeds, the results are significantly improved using the proposed Type-2 fuzzy Logic Handover T2FLH system to model uncertainty and imprecision in a better way. Relying on T2FLH has led to a decrease in the rate of Packets Loss and Delay because the selection of the available services with previously specified time in the neighboring table became more accurate and avoiding uncertainty, depending on calculating the size of the data and the WFD signal strength conjunction with the distance and speed between the vehicles.

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%.

Background: With the explosion of data in recent years, recommender systems have become increasingly important for personalized services and enhancing user engagement in various industries, including e-commerce and entertainment. Collaborative filtering (CF) is a widely used approach for generating recommendations, but it has limitations in addressing issues such as sparsity, scalability, and prediction errors. Methods: To address these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines an incremental singular value decomposition approach with an item-based ontological semantic filtering approach in both online and offline phases. The ontologybased technique improves the accuracy of predictions and recommendations. The proposed method is evaluated on a real-world movie recommendation dataset using several performance metrics, including precision, F1 scores, and MAE. Results: The results demonstrate that the proposed method outperforms existing methods in terms of accuracy while also addressing sparsity and scalability issues in recommender systems. Additionally, our approach has the advantage of reduced running time, making it a promising solution for practical applications. Conclusion: The proposed method offers a promising solution to the challenges faced by traditional CF methods in recommender systems. By combining incremental SVD and ontological semantic filtering, the proposed method not only improves the accuracy of predictions and recommendations but also addresses issues related to scalability and sparsity. Overall, the proposed method has the potential to contribute to the development of more accurate and efficient recommendation systems in various industries, including e-commerce and entertainment.

Background: If we rely solely on whether to assign permissions together to determine roles, the roles we generate may not necessarily reflect the needs of the system. Therefore, the role generation process can be done based on user-to-permission dynamic relationships, such as user dynamic operation logs, thus providing the motivation for this work. Methods: In our paper, we introduce a special generalization process and a frequent set-based analysis method to generate roles based on the particular data type of user dynamic operation logs so that the time factor of permissions used is considered before the process of role generation to generate the roles such also as auth_perms(r) = {p1, p2, p3}. Results: Our algorithm is less time consuming and generates less roles than traditional algorithm. Furthermore, the roles generated by the algorithm can better describe the real needs of the system and have better interpretability. Conclusion: The results show that the algorithm has superior performance and useful role generation compared to traditional algorithm.