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- Volume 16, Issue 2, 2023
Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) - Volume 16, Issue 2, 2023
Volume 16, Issue 2, 2023
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An Infrared and Visible Image Fusion Approach of Self-calibrated Residual Networks and Feature Embedding
Authors: Jinpeng Dai, Zhongqiang Luo and Chengjie LiBackground: The fusion of infrared images and visible images has been a hot topic in the field of image fusion. In the process of image fusion, different methods of feature extraction and processing will directly affect the fusion performance. Objectives: Low resolution (small size) of high-level features will lead to the loss of spatial information. On the other side, the low-level features are not significant due to their insufficient filtering of background and noise. Methods: In order to solve the problem of insufficient feature utilization in existing methods, a new fusion approach (SC-Fuse) based on self-calibrated residual networks (SCNet) and feature embedding has been proposed. The method improves the quality of image fusion from two aspects: feature extraction and feature processing. Results: First, self-calibrated modules are applied to the field of image fusion for the first time, which enlarged the receptive field to make feature maps contain more information. Second, we use ZCA (Zero-phase Component Analysis) and l1-norm to process features, and propose a feature embedding operation to realize the complementarity of feature information at different levels. Conclusion: Finally, a suitable strategy is given to reconstruct the fused image. After ablation experiments and comparison with other representative algorithms, the results show the effectiveness and superiority of SC-Fuse.
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Securing Hybrid Architecture of Cloudlet Computing in 5G Networks Enabling IoT and Mobile Wireless Devices
Authors: Tarek S. Sobh and Awad H. KhalilAims: This research aims to secure and support mobile devices and IoTs enabled in WLAN and 5G Multi-Access Edge Computing (MEC) infrastructures. Background: Currently, wireless network access gains increasing potential in today's networks. At the same time, such ongoing wireless network access also raises the risk of network attacks. The 5G technology is expected to empower people's inter-communications by integrating wireless networking technologies in all networks. Additionally, the emerging 5G technologies put forward many new requirements for RF characteristics and techniques regarding bandwidth and power issues, in addition to supporting high connectivity capacity for the emerging IP-based IoT applications. Objective: The objective is to provide a hybrid architecture of the proposed framework for a 5G network with cloudlet computing. The purpose that we are focusing on it is to support and monitor wireless networks under attack. Methods: The solution is done through a hybrid architecture. This architecture integrates the cloudlet and Wi-Fi Access Points (AP) to provide an integrated Wi-Fi-enabled cloudlet. On the other hand, we have specified a solution for detecting unauthorized APs by using authentication and authorization techniques for securing communications among endpoint devices such as IoT devices or mobile devices, cloudlet, and the main cloud. A framework of this architecture has been developed to face the issue of securing IoT devices and mobile wireless devices. Results: The traffic sniffing and traffic filtering of the endpoints are done. Therefore, the required actions that define the organization's policy are applied through the permitted access list. In addition to a wireless network location, the management components use more than one authorized attribute value to generate the authorized list, such as SSID, MAC, BSSID …etc. Finally, Traffic analysis provides the user to get reports, statistics, and analysis charts to secure endpoints activities. Conclusion: We are focusing on one of the critical security issues of wireless networks. Namely, the issue of unauthorized access becomes more critical due to getting access to a network without subscriber permission. Moreover, the proposed solution allows managing access control permissions to allow or block access to resources for the user of a mobile device.
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A Retrieval Method for Spatiotemporal Information of Chorography Based on Deep Learning
More LessBackground: On retrieving Spatiotemporal Chorography (STIC) information, one of the most important topics is how to quickly pinpoint the desired STIC text out of the massive chorography databases. Domestically, there are no diverse means to retrieve spatiotemporal information from the chorography database. Emerging techniques like data mining, Artificial Intelligence (AI), and Natural Language Processing (NLP) should be introduced into the informatization of chorography. Objective: This study intends to devise an information retrieval method for STIC based on deep learning and fully demonstrates its feasibility. Methods: Firstly, the authors explained the flow for retrieving and analyzing the data features of STIC texts and established a deep hash model for STIC texts. Next, the data matching flow was defined for STIC texts, the learned hash code was adopted as the memory address of STIC texts, and the hash Hamming distance of the text information was computed through linear search, thereby completing the task of STIC retrieval. Results: Our STIC text feature extraction model learned better STIC text features than the contrastive method. It learned many hash features and differentiated between different information well when there were many hash bits. Conclusion: In addition, our hash algorithm achieved the best retrieval accuracy among various methods. Finally, the hash features acquired by our algorithm can accelerate the retrieval speed of STIC texts. These experimental results demonstrate the effectiveness of the proposed model and algorithm.
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Comparison of Soft Computing and Optimization Techniques in Classification of ECG Signal
Authors: Prerak Mathur, Pooja Sharma and Karan VeerElectrocardiogram (ECG) is a visual representation of the heartbeat that can be used to detect cardiac problems. It helps in detection of a normal or abnormal state of heart diseases. Therefore, it is difficult to detect the cardiological status with naked eyes. So, features extraction from ECG signal is crucial to recognise heart disorders. After selecting significant features, classification can be done by Machine Learning (ML), and Deep Learning (DL). Most of the methods utilised to classify the electrocardiogram are based on 1-D electrocardiogram data. These methods focus on extracting the attributes wavelength and time of each waveform as input but but their behavior remains different during classification. Various ECG construal algorithms based on signal processing approaches have been planned in recent years. Few studies show how optimisation techniques are helpful for feature selection and classification with ML and DL. This work compares the studies based on ML and DL. It also depicts how optimisation methods increase the accuracy, sensitivity, and specificity of data.
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Assessment of Various Scheduling and Load Balancing Algorithms in Integrated Cloud-Fog Environment
Authors: Jyotsna and Parma NandCloud computing is a rapidly developing computing technology, which enables to process and store large volumes of information along with providing services to numerous end users across various disciplines. Due to ever-increasing demand with the introduction of technologies like IoT, cloud servers processing capability and storage capacity are rapidly saturating, creating lag in response time. The lag in response time contradicts the never-ending rise of demand for programs associated with real-time analytics and real-time applications. Technologies like Fog computing which works in close association with cloud computing can act as an alternative platform to meet the desired goals. However, due to limited capacity, fog computing-based platforms get exhausted easily. Setting up effective burden allocation algorithms will allow for powerful and well-organized use of both of these platforms. Numerous methods are there to address problems such as task scheduling, resource scheduling, workflow scheduling, load balancing, resource provisioning, and load balancing. The present study compares different aspects of the existing techniques. The current study also explores the quality of service metrics across a variety of existing techniques, providing food for further experimentation. Background: It is required to design a suitable scheduling algorithm that enhances the timely execution of goals such as load distribution, cost monitoring, minimal time lag to react, increased security awareness, optimized energy usage, dependability, and so on. In order to attain these criteria, a variety of scheduling strategies based on hybrid, heuristic, and meta-heuristic techniques are under consideration. Objective: IoT devices and a variety of network resources make up the integrated cloud-fog environment. Every fog node has devices that release or request resources. A good scheduling algorithm is required in order to maintain the requests for resources made by various IoT devices. Methods: This research focuses on the analysis of numerous scheduling challenges and techniques employed in a cloud-fog context. This work evaluates and analyses the most important fog computing scheduling algorithms. Results: The survey of simulation tools used by the researchers is done. From the compared results, the highest percentage in the literature has 60% of scheduling algorithm which is related to task scheduling and 37% of the researchers have used iFogSim simulation tool for the implementation of the proposed algorithm defined in their research paper. Conclusion: The findings in the paper provide a roadmap of the proposed efficient scheduling algorithms and can help researchers to develop and choose algorithms close to their case studies.
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Contemporary Approaches to Analyze Non-stationary Time-series: Some Solutions and Challenges
Authors: Ankit Dixit and Shikha JainEnhancement of technology yields more complex time-dependent outcomes for better understanding and analysis. These outcomes generate more complex, unstable, and highdimensional data from non-stationary environments. Hence, more challenges are arising day by day to fulfill the increasing demand for future estimation. Thus, in this paper, an extensive study has been presented to comprehend the statistical complexity and randomness of Non-Stationary Time Series (NS-TS) data at the atomic level. This survey briefly explains the basic principles and terms related to Non-Stationary Time Series (NS-TS). After understanding the fundamentals of NS-TS, this survey categorized non-stationary time series into groups and subgroups based on a change in statistical behavior. It also provides a comprehensive discussion on contemporary approaches proposed by researchers in each category of non-stationarity. These algorithms include clustering, classification, and regression techniques to deal with different types of domains. Every category of non-stationarity consists of a separate table to draw some advantages and disadvantages of existing approaches. At the end of each non-stationarity type, a short discussion and critical analysis have been done. In the conclusion section, it is observed that this research sphere still has many open challenges that need to be addressed and demand more exploration. Furthermore, it discusses the possible solution of improvisation in future research.
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A State of the Art Review on User Behavioral Issues in Online Social Networks
Authors: Nidhi A. Patel and Nirali NanavatiSocial networks are aimed at information sharing and friend-making due to the rapid development of Online Social Networks (OSN) and the increasing number of online users. The OSNs are also becoming an ideal platform for merchandise recommendation, opinion expression, information diffusion, and influence generation. Different types of social network services and users select the appropriate social network technology, services, and applications to meet their sociability, entertainment, or information retrieval needs. User behavior involves user interaction, access, and browsing of the OSN. The users have different roles in different groups of social networks. Different identifications of the user may cause the user's intention to change. The user's intention may change as a result of different identifications. In this work, we discuss an introduction to OSN, single and multi-platform user behavior with various prediction models and recommendations.
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Deep Image Segmentation Using a Morphological Edge Operator
Authors: Mei Zhang, Bin Xu and Jinghua WenBackground: Segmentation of deep images is a difficult, persistent problem in the computer vision field. This paper aimed to address the defects of traditional segmentation methods with deep images, presenting a deep image segmentation algorithm based on a morphological edge operator. Methods: Deep image edge features were first extracted using three traditional edge operators; the edge and tip type jump edges were then extracted via a morphological edge operator, which was used to make the boundary connection; finally, to obtain more accurate segmentation results, skeletonizing was used to refine the image. Results: Compared with traditional segmentation algorithms, the improved algorithm obtained smooth and continuous boundaries, protected edge information from blurring, and was slightly more efficient. When Mickey Mouse depth images were used as experimental subjects, the computational time was reduced by 12.62 seconds; when rabbit depth images were used, computational time was reduced by 17.53 seconds. Conclusion: Morphological edge operator algorithm proposed in this paper is much more effective than traditional edge detection operators algorithms for deep image segmentation; it can clearly divide Mickey Mouse's ears, eyes, pupils, nose, and mouth.
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