Search

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy.

Motivated by the Arhangel?skii ?s-Lindel?f cardinal function? definition, Kocinac, Konca, Bonanzinga, Maesano and Singh defined and studied the set covering properties. In this paper, we study some set versions of compact-type properties and give partial solution to a problem posed by Singh in [11].

Heterogeneous wireless networks are an integration of dissimilar networks for better coverage and spectral efficiency. These networks differ in cost, reliability, bandwidth, coverage, security, and other related parameters. And cell users roam across these networks and gets connected to the new candidate network during handoff to maintain call continuity for uninterrupted service delivery. But the decision of selecting an optimal network in such a diverse environment is a challenge. To cater this, Handoff Optimization Algorithm (HOA) having two phases is proposed-Handoff Initialization phase and Network Selection or Decision making phase. This paper discusses the network selection phase of Handoff Optimization Algorithm (HOA) for Future Networks (FN). Six key parameters based on network, mobile and user interest are selected after careful analysis and understanding. Fuzzy Analytical Hierarchical Process (FAHP) is used to assign weights to these key parameters and then Elimination and Choice Expressing Reality (ELECTRE) is applied to rank different networks. Results show that decision making phase of HOA selects the optima network from among the candidate networks based on network, mobile or user inputs.

Seamless integration of the new and existing networks is required to create next generation wireless networks capable of providing wide range of services coupled with uninterrupted connectivity and high speeds offering unprecedented quality of service to users. Smooth and proficient handoff techniques are important to achieve optimal network performance in heterogeneous wireless environments. This paper proposes Multiple Criteria Decision Making (MCDM) techniques of both non-fuzzy (Technique for Order of Performance by Similarity to Ideal Solution (TOPSIS)) and fuzzy (Fuzzy TOPSIS (FTOPSIS)) type for network ranking. The proposed algorithm uses priority ranking to assign weights to six parameters by using Fuzzy Analytic Hierarchy Process (FAHP). Further, TOPSIS and FTOPSIS techniques are compared for various traffic classes and used for network selection in the prevalent network conditions. The results show that contingent on the traffic classes, the FTOPSIS selects the ideal network with diminished ping-pong rate and call blocking probability as compared to TOPSIS while accounting for the uncertain environment.

The internal participating nodes in mobile network can be affects fully or partially by selfish nature to improve its own communication rate. This nature can degrade the forwarding rate and affects the overall performance of the network. The detection of such selfish nodes is required to improve the communication throughput and network performance. In this paper, an Agent based analysis approach is defined to detect the selfish node in the network. The fuzzy based zonal analysis method is suggested to perform the zonal evaluation on the mobile network. The distributed Agents performed the analysis on the fuzzy based communication parameters. The parameter specific evaluation method is able to separate the good and selfish nodes. Once the selfish nodes are identified, the preventive route formation is done for safe communication. In this paper, the Agent adaptive model is presented for detection and prevention of selfish nodes. The proposed Agent adaptive model is simulated in NS2 environment. The comparative results are generated against token based, agent based and watchdog methods. The analysis results show that the proposed protocol has achieve the 17% improvement in packet communication, 37% reduction in communication loss and 53ms reduction in packet delay.

Cloud security is one of the major issues which are affecting the acceptance of Cloud computing (CC). In spite of the number of benefits offered by CC, it also suffers from a myriad of security vulnerabilities. Security is the prime concern while we are accessing the resources from the Cloud through the Internet. This research work is primarily on IAAS (Infrastructure as a service). Here a protocol called Authenticated Routing on Cloud Network (ARCN) has been proposed, to secure the communication route between the client and service provider. The proposed protocol ARCN uses the public key cryptography to mitigate the various security attacks like malicious insider attacks, Spoofing, Falsified routes, DoS, etc.

Due to increasing applications which need security, face is the most natural way to recognize the person. This paper presents the performance analysis of different face recognition techniques. Linear Binary Pattern (LBP), Gabor Wavelet, Histogram of Oriented Gradient (HOG) techniques are used for feature extraction on two standard databases of images i.e., ORL and Yale Face database. Then Principal Component Analysis (PCA) is applied for feature selection with six different distance metric functions, Cosine, Euclidean, Correlation, Cityblock, Spearman and Minkowski for similarity matching of images.

Wireless communication technologies has gone through continuous evolution leading to unparalleled growth of wireless networks in terms of their reach, capacity and the range of services provided by them. Wireless networks are now capable of providing secure and reliable services at great speeds. As wireless service providers aspire to provide the user with best quality of service and seamless connectivity it is imperative that various kind of networks based on different technologies are integrated together to provide the heterogeneous wireless environment in which a user can roam freely while being connected to the network that best caters to the service and connectivity requirements. In such an environment efficient handoff is the key requirement for providing seamless mobility and connectivity. For an effective handoff decision suitable parameters need to be chosen. The authors here propose Handoff Optimization using Priority Ranking (HOPR) algorithm which selects six parameters on the basis of priority ranking and then assigns them weights using fuzzy and non-fuzzy MCDM techniques.