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Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) plays a vital role in detecting intruder attacks, and it is also used to monitor the traffic in the network. The paper is aimed to control the attacks using the machine learning (ML) technique integrated with the artificial bee colony (ABC) named Group-ABC (G-ABC). The IDS detector has been implemented and further simulation results have been determined using the G-ABC. The evaluation has been carried out using the measures such as precision, recall, accuracy, and F-measure. Different attacks such as user to root (U2R), probe, root to local (R2L), backdoors, worms, and denial-of-service (DoS) attacks have been detected. The simulation analysis is performed using two datasets, namely, the NSL-KDD dataset and UNSW-NB15 dataset, and comparative analysis is performed against the existing work to prove the effectiveness of the proposed IDS. The objective of the work is to determine the intruder attacker system using the deep learning technique.

Cardiac diseases are most prevalent these days with high mortality ratio. The causes and symptoms of the heart diseases also vary according to the type of heart disease. In recent years, Heart disease diagnosis has attracted researchers to provide some automated and online solutions to detect heart disease at an early stage. AI and machine learning algorithms have already contributed a lot in this field and have been proved to be reliable and most efficient. Various feature weight identification and optimization heuristics have also been integrated with machine learning models for detecting heart disorders accurately. This review encapsulates the study on the research works to optimize various stages of machine learning models. It also intends to discuss the significance of pre-processing specially feature selection for machine learning algorithm in detail. The recent advancements in machine learning algorithms, methodologies and the performance gain are also provided in this article.

The cyclic graph [Formula: see text] of a semigroup [Formula: see text] is the simple undirected graph whose vertex set is [Formula: see text] and two vertices [Formula: see text] are adjacent if the subsemigroup generated by [Formula: see text] and [Formula: see text] is monogenic. In this paper, we determine the clique number of [Formula: see text] for an arbitrary semigroup [Formula: see text]. Further, we obtain the independence number of [Formula: see text] if [Formula: see text] is a finite monogenic semigroup. At the final part of this paper, we give bounds for independence number of [Formula: see text] if [Formula: see text] is a semigroup of bounded exponent and we also characterize the semigroups attaining the bounds.

Radiology reports often contain follow-up imaging recommendations, but failure to comply with them in a timely manner can lead to delayed treatment, poor patient outcomes, complications, and legal liability. Using a dataset containing 2,972,164 exams for over 7 years, in this study we explored the association between recommendation specificity on follow-up rates. Our results suggest that explicitly mentioning the follow-up interval as part of a follow-up imaging recommendation has a significant impact on adherence making these recommendations 3 times more likely (95% CI: 2.95 - 3.05) to be followed-up, while explicit mentioning of the follow-up modality did not have a significant impact. Our findings can be incorporated into routine dictation macros so that the follow-up duration is explicitly mentioned whenever clinically applicable, and/or used as the basis for a quality improvement project focussed on improving adherence to follow-up imaging recommendations.

Exponential growth has been measured in the size of data during the last two decades. The mining of utility itemsets from a large dataset is a challenging issue because of the diverse dimensions of data. Various itemset mining algorithms have been projected by the researchers to discover relations among the items of a database. In this paper, a systematic literature review has been presented for different algorithms, which are being used for utility itemset mining. 37 studies have been selected to answer the research questions framed for this review based on different methods of mining. These methods have been sorted into four categories with their benefits, drawbacks, performance, and scalability. It has been concluded that research efforts should be geared towards more scalable, secure, and safe methods that can operate more meritoriously on large datasets.

The commuting graph $$\varDelta (G)$$ of a finite non-abelian group G is a simple graph with vertex set G, and two distinct vertices x, y are adjacent if $$xy = yx$$ . In this paper, first we discuss some properties of $$\varDelta (G)$$ . We determine the edge connectivity and the minimum degree of $$\varDelta (G)$$ and prove that both are equal. Then, other graph invariants, namely: matching number, clique number, boundary vertex, of $$\varDelta (G)$$ are studied. Also, we give necessary and sufficient condition on the group G such that the interior and center of $$\varDelta (G)$$ are equal. Further, we investigate the commuting graph of the semidihedral group $$SD_{8n}$$ . In this connection, we discuss various graph invariants of $$\varDelta (SD_{8n})$$ including vertex connectivity, independence number, matching number and detour properties. We also obtain the Laplacian spectrum, metric dimension and resolving polynomial of $$\varDelta (SD_{8n})$$ .

Background/Objectives: Current wave of technological innovations like IoT/Green IoT (G-IoT) has opened doors for new possibilities that must be explored for achieving Sustainable Development Goals (SDG’s) designed by United Nations. The main objective of the study is to propose an inference regarding potential role of IoT in achieving the target of sustainable development worldwide. Methods/Statistical analysis: An Online survey among the IoT industry experts, researchers, faculty members and educationists working in the area of IoT of ‘A’ grade state university in India has been conducted using ‘Google Forms Questionnaire’ to gain an insight into significance of G-IoT for sustainable development. The number of survey participants were around 300. Findings: The study attempts to track the gaps, challenges and obstacles in attaining the target of SDG’s. The major findings of the paper depict the significance of IoT in achieving the SDG’s as 84% respondents believes that that Green IoT/ Green ICT technologies can act as a game changer in accomplishing sustainable development and growth of green economy. Major areas where we can take advantage of these technologies include productive and efficient utilization of raw materials along with preserving natural resources and minimizing the emission of greenhouse gases and other wastes. Novelty/Applications: A sustainable environment around the world demands the planned and organized usage of IoT and G-IoT concept towards constructing/developing the goods and services for enhancing renewable energy sources, energy-conserving computing, management of power sources, green metrics, assessment tools and methodologies have potential to significantly transform the traditional development into sustainable development. Keywords: Sustainable development; Green IoT; IoT devices; technological innovations

X-ray diffraction (XRD) techniques are powerful, non-destructive characterization tool with minimal sample preparation. XRD provides the first information about the materials phases, crystalline structure, average crystallite size, micro and macro strain, orientation parameter, texture coefficient, degree of crystallinity, crystal defects etc. XRD analysis provides information about the bulk, polycrystalline thin films, and multilayer structures, which is very important in various scientific and material engineering fields. This review discusses the diffraction related phenomena/principles such as powder X-ray diffraction, and thin-film/grazing incidence X-ray diffraction (GIXRD) comprehensively for thin film samples which are used frequently in various branches of science and technology. The review also covers few case studies on polycrystalline thin-film samples related to phase analysis, preferred orientation parameter (texture coefficient) analysis, stress evaluation in thin films and multilayer, multiphase content identification, bifurcation of multiphase on multilayer samples, depth profiling in thin-film/ multilayer structures, the impact of doping effect on structural properties of thin films etc., comprehensively using GIXRD/XRD.

Utility Itemset Mining (UIM) is a fundamental technique to find out various itemsets with interestingness measures in addition to their quantity. It helps in finding valuable items that cannot be tracked with frequent itemset mining. There are many techniques to mine the itemsets based on their utilities, but the need of the hour is to mine them from larger datasets. This paper presents a brief overview of various approaches for utility mining, which mine using the parallel framework to enhance the pace of computation. The paper is concluded with a discussion on various challenges and openings in the field of parallel mining and provides away for further development of the prevailing methodologies of big data.

The enhanced power graph Pe(G) of a group G is a graph with vertex set G and two vertices are adjacent if they belong to the same cyclic subgroup. In this paper, we consider the minimum degree, ind...

Generally, it is very difficult to grow large diameter 4H-SiC single crystal with single polytype by Physical Vapor Transport (PVT) growth method and mostly it ends up with the presence of some other polytypes (viz. 6H, 15R). This paper presents the various comprehensive polytype identification techniques in SiC wafer grown by PVT method. Characterization techniques, viz. X-Ray diffraction, Scanning electron microscopy, Cathodoluminescence (CL) and Raman spectroscopy, are used in the present study in order to identify the presence of 4H, 6H and 15R polytype region in SiC wafer. Raman mapping (using phonon frequencies at 150, 171, 203 cm−1 for 6H, 15R and 4H, respectively) and X-Ray Topography [using grazing incidence asymmetric plane (11–2 8) for 4H-SiC, (11–2 12) for 6H-SiC and (11–2 30) for 15H-SiC] and CL spectra (defect state peak positions at ~ 500 nm and 580 nm for 4H and 6H, respectively) are proposed to distinguish the switching of polytypes in a large area SiC wafer. The polytype switching in SiC ingot may occur because of temperature fluctuations during the sublimation growth process.

The cyclic graph $$\Gamma (S)$$ of a semigroup S is the simple graph whose vertex set is S, two element being adjacent if the subsemigroup generated by these two elements is monogenic. The purpose of this note is to prove that the chromatic number of $$\Gamma (S)$$ is at most countable. The present paper generalizes the results of Shitov (Graphs Comb 33(2):485–487, 2017) and the corresponding results on power graph and enhanced power graph of groups obtained by Aalipour et al. (Electron J Comb 24(3):#P3.16, 2017).

AlN nucleation layers (NL) with different thickness were grown on 4H-SiC substrates using MOVPE. The growth evolution of the AlN layer on SiC was investigated. The effect of fully strained AlN nucleation layer (NL) on the AlN/SiC interface and on the subsequent GaN growth was also investigated. The thickness of the NL was varied between 10 nm and 100 nm in order to study the surface morphology and strain of AlN NL layer. The interface quality between AlN and SiC was characterized using HRXRD. It was observed that 100 nm NL was partially relaxed whereas 40 nm AlN NL was fully strained with improved SiC/AlN interface. GaN layer grown with fully strained NL showed improved surface morphology and lower screw dislocation density. 2DEG properties of AlGaN/GaN HEMT structures grown on fully strained and partially relaxed NL were found to be almost similar.

Purpose To compare the Vancouver risk calculator (VRC) with American College of Radiology (ACR) Lung Imaging Reporting and Data System (Lung-RADS) in predicting the risk of malignancy in the National Lung Screening Trial (NLST). Materials and Methods A total of 2813 patients with 4408 nodules (4078 solid, 330 subsolid) were available from the NLST for evaluation. Nodules were scored by using VRC with nine parameters (output was the percentage likelihood of malignancy; VRC threshold for malignancy likelihood set as greater than 5%) and Lung-RADS (output was category 2-4B; malignancy defined as category 4A or 4B; malignancy likelihood greater than 5%). Lung-RADS and VRC were compared for sensitivity, specificity, and accuracy for malignancy on a per-nodule and per-patient basis. Results Of 4408 total nodules, 100 of 4078 (2.5%) solid nodules were malignant and 10 of 330 (3%) subsolid nodules were malignant. On an overall per-nodule basis, the sensitivity, specificity, and accuracy for VRC and Lung-RADS were 93%, 90%, and 90% for VRC and 87%, 83%, and 83% for Lung-RADS, respectively (P = .077, P < .001, and P < .001, respectively). On a per-patient basis, the sensitivity, specificity, and accuracy for VRC and Lung-RADS were 93%, 85%, and 85% for VRC and 87%, 76%, and 76% for Lung-RADS, respectively (P = .077, P < .001, and P < .001, respectively). Conclusion The Vancouver risk calculator had superior overall accuracy than the Lung Imaging Reporting and Data System in predicting malignancy in the National Lung Screening Trial for total nodules, as well as on a per-patient basis. © RSNA, 2019 See also the editorial by Black in this issue.

Mesoporous structures with interconnecting nano-fibers (MSINs) are realized by irradiating low energy (50, 75, and 100 keV) beams of Ar + -ions normal to the surface of molecular beam epitaxially grown gallium antimonide (GaSb) epi-layer deposited on (100) oriented semi-insulating gallium arsenide (GaAs) substrates. The irradiation of 100 keV Ar + -ion beam at fluence of 1 × 10 17 ions/cm 2 provides optimal MSIN structure with distinct nanofibrous network. A physical model for evolution of MSINs with irradiation of energetic Ar + -ions at different fluences onto GaSb surface is illustrated as creation of point defects in the form of vacancy and interstitial sites. The ion tracking simulation using the Stopping and Range of Ions in Matter and Transport of Ions in Matter (SRIM-TRIM-2013) software resembles the formation of MSINs with Ar + -ion irradiation on GaSb epi-layer. The electron-microscopic, spectroscopic, and crystallographic analysis of the MSINs suggest the presence of nanocrystallites, high degree of oxidation, co-existence of gallium oxide (Ga 2 O 3 ) and antimony oxide (Sb 2 O 3 , Sb 2 O 5 ), and presence of elemental antimony (Sb) phase in the nanofiber interconnects.

Latest technologies and the upheaval of Internet of Things (IoT) have fueled research in almost all areas related to human beings, disseminating anytime, anywhere, any device information access to human beings in different ways. This phenomenal research in recent decades aims to synergize human, data, processes and things, organizations, places, services, and facilities together in exceptional ways. Although IoT offers ample benefits to human society, still the process of manufacturing, utilization, implementation, and distribution of IoT services and devices requires voluminous energy and resources, ultimately producing ever- increasing toxic electronic waste. To mitigate the potential negative impacts of the latest scientific developments in the area of IoT on human society and environment, it has become mandatory to effectively deal with the threats and challenges posed by IoT. These challenges mainly include enhanced energy consumption, generation of electronic waste, emission of greenhouse gases (mainly CO2), utilization of non-biodegradable materials for IoT devices, usage of non-renewable and natural raw materials. This situation has created a need to move towards Green IoT (G-IoT), a future technological enhancement of IoT associated with green technology and the green economy. Green IoT is aimed towards bringing notable improvements in environmental and human well-being so as to make the world smarter using sustainable technological developments. G-IoT is the need of the hour and it carries the potential to completely transform human society along with improving environmental health. G-IoT is the latest technology that emphasizes developing solutions towards eliminating or mitigating the negative influences on people’s health and the environment. It basically focuses on two aspects. The first aspect is oriented towards designing IoT computing devices, communications protocols, and networking architectures that are energy efficient. The second aspect is oriented towards leveraging IoT technologies to reduce emissions of greenhouse gases, radiation, and pollution. Using the technological advancements in IoT enabling technologies, G-IoT has great capability to strengthen environmental and economic sustainability. This chapter presents an overview of the need and importance of green technologies and green processes in sustainable development and building a smart world. It goes on to explore the principles and applications of G-IoT in the growth of the society by examining its potential to enhance the quality of human life, economic growth, the environment, and green global modernization.