Comparison Table Research Articles

MQTT Implementations, Open Issues, and Challenges: A Detailed Comparison and Survey

Abstract: MQTT is an open standard protocol promoted by OASIS and ISO, which allows devices to transport messages using the publish/subscribe model. MQTT is more prevalent than other application layer protocols of the Internet of Things (IoT) due to its lightweight nature, low bandwidth usage, application demand, etc. It is easy and straightforward to use the protocol, making it optimal for communication in resource-constrained situations such as machine-to-machine (M2M), Wireless Sensor Networks (WSNs), and in IoT circumstances in which the actuator and sensor nodes connect with applications through the MQTT message broker. A few review papers on MQTT protocol are available in the literature that focuses on broker details, comparison of IoT protocols, and limitations. In this paper, an overview of MQTT, existing survey work on MQTT, publication statistics, MQTT protocol performance evaluation, applications of MQTT, security issues of MQTT, comparison between MQTT and MQTT-SN, tools available or MQTT and available MQTT brokers to provide service are discussed. Graphs and comparison tables are presented to show the outcomes of the application and performance evaluation. The scope of this review paper is also to contribute a novel taxonomy of application layer protocols, their merits and demerits, correlation of MQTT with other application layer protocols, existing works of MQTT protocol to improve reliability, efficiency, security, issues, and challenges in MQTT, as well as future directions of MQTT.

Patient-reported satisfaction with care and care coordination: Results from an online survey conducted in Manitoba, Canada during the COVID-19 pandemic.

258 Background: In Manitoba, Canada, changes to cancer care delivery implemented in response to the COVID19 pandemic included, but were not limited to, restrictions on informal caregiver accompaniment for ambulatory care appointments and implementation of physician care administered over the telephone. These changes were predicted to negatively impact the patient experience, specifically patient satisfaction with care and perceived care coordination. To assess this, a survey of patients on active treatment was conducted. Methods: After ethics approval, a SurveyMonkey survey was conducted of cancer patients receiving radiation and IV treatment at one of the 24 cancer treatment sites across Manitoba. The survey collected demographics, disease characteristics, functional status and responses to validated questionnaires addressing satisfaction with care (PSCC), and care coordination (CCQP, including communication (CCQPcomm) and navigation (CCQPnav) subscales). Mean differences of PSCC and CCQP scores (including subscales) were compared using t-test for dichotomous patient characteristics and ANOVA followed by post-hoc pairwise t-tests for non-dichotomous categorical patient characteristics with statistically significant ANOVA results. Results: Between July 2020 and February 2022, 203 responses were collected. The 154 respondents with complete responses for either PSCC, CCQP or both were included in this analysis. Median age of respondents was 64.6 (SD = 11.7). Sex was balanced (male = 47%, female = 52.3%, other/prefer not to say = 0.7%). Breast (26.1%), hematological (13.7%), and prostate (13.1%) cancers were the most common. PSCC and CCQP scores did not differ by marital status, treatment site, disease type, or treatment intent (i.e., curative versus non-curative). Individuals with lower functional status (i.e., ECOG > 1) were identified to have lower mean scores for CCQP and CCQPcomm. Patients < 59 years had lower mean PSCC scores compared to those 59 – 69, and 70+. See Table for comparison of CCPQ and PSCC scores by age and functional status. Conclusions: Young patients and those with decreased functional status were identified to have statistically significant lower mean scores for PREMs reflecting satisfaction and coordination of care. Further work to identify meaningful strategies to improve the patient experience for these two patient groups is needed.[Table: see text]

A Novel Operator to Solve Decision-Making Problems Under Trapezoidal Fuzzy Multi Numbers and Its Application

This article investigates solutions to multiple attribute decision-making (MADM) problems in which the attribute values take the form of trapezoidal fuzzy multi-numbers. To do this, this paper proposes a kind of mean aggregation operator called the Bonferroni harmonic mean operator for aggregating trapezoidal fuzzy information. Then, an approach that is a solution algorithm has been developed to find a solution to multi-attribute decision-making problems. Afterwards, an illustrative example has been given to verify the developed approach and to show its usefulness and efficiency. Finally, a comparison table has been presented to compare the proposed method with some existing methods.

An unsupervised cluster-based feature grouping model for early diabetes detection

Diabetes mellitus is often a hyperglycemic condition that poses a substantial threat to human health. Early diabetes detection decreases morbidity and mortality. Due to the scarcity of labeled data and the presence of oddities in diabetes datasets, it is exceedingly difficult to develop a trustworthy and accurate diabetes prognosis. The dataset and groupings of the features using the elbow and silhouette methods have been clustered using K-means. Various machine learning approaches have also been applied to the cluster-based dataset to predict diabetes. We propose an unsupervised cluster-based feature grouping model for early diabetes identification using an open-source dataset containing the data of 520 diabetic patients. On the cluster-based dataset and the complete dataset, the maximum Accuracy (ACC) is 99.57% and 99.03%, respectively. The best Precision, Recall, minimum mean squared error (MSE), maximum mean squared error (MSE), and F1-Score of 1.000 are obtained from multi-layer perceptron (MLP), random forest (RF), and k-Nearest Neighbors (KNN), 0.984 from random forest (RF) and support vector machine (SVM), 0.010 from RF, 0.067 from KNN, and 99.20% from RF, respectively. A comparison table displays the anticipated outcomes and highlights the aspects of this research that are most likely to occur as intended. The preprocessed data and codes are available on the GitHub repository to https://github.com/mhashiq/Early-stage-diabetes-risk-prediction.

Mosladadoensis (Lamiaceae), a new species from the southern islands of South Korea.

Mosladadoensis (Lamiaceae), a new species from the southern islands of South Korea, is described and illustrated. The new species is morphologically similar to M.chinensis, but is distinguished from the latter by having two types of hairs on its stems, wider leaf blades, longer corolla length, and ellipsoid nutlets with a narrowly U-shaped extended area of abscission scar. Mosladadoensis is also distinguished from the Chinese narrow endemic M.hangchouensis by having an included pistil to the corolla, smaller ellipsoid nutlets, and later flowering and fruiting season. Phylogenetic analyses, based on two nuclear ribosomal (ETS, ITS) and three chloroplast (rbcL, matK, trnL-F) DNA regions, confirmed that the new species was constructed as monophyletic, and that M.dadoensis and M.hangchouensis form a sister group with robust support. We hereby provide a detailed morphological description of M.dadoensis with its corresponding geographical distributions, and comparison tables of related taxa.

Comparative analysis between modulation instability in an erbium and non-erbium optical fiber with generalized external potentials

The effects of two-level resonant atoms and self-steepening effect on modulation instability in an erbium fiber is studied by considering the nonlinear Schrödinger Maxwell–Bloch (GINLS–MB) with generalized external potentials. By applying linear stability analysis (LSA), we examine the comparison between MI gain spectra for both with and without erbium effects. We depict certain significant results by graphically illustrating the 2D and 3D figures through suitably selecting the parameters. In addition, we provide a comparison table for adopting parameters. Obtained remarkable results are useful for the study of soliton stability in nonlinear optical communication system. Also, our investigations shows that the gain spectrum mainly influenced by erbium atoms and self-steepening term. Especially, symmetric manner of gain spectrum becomes as asymmetric due to the impact of self-steepening term. The theoretical findings carried out in this work will be beneficial for experimental observation of MI process in an erbium doped fiber.

A Review on Comparison of Different Water Leak Detection Techniques Suitable for Indian Scenario

Effective management of water distribution is necessary for any country worldwide by reducing water losses during different utility operations. Also in India, water loss is the biggest challenge for the municipalities and local bodies of various states. Different researchers had tried to give attention to this issue and its effective solution through different techniques. In this research paper a summarization of different methods used to identify and locate the leakage with its merits and demerits was given. A review of different leak detection techniques from randomly selected research papers in the form of a comparison table considering suitability, methodology, outcomes, and limitations by different researchers suitable for the Indian scenario was also presented. In the end, an effort was made to emphasize the selection of the appropriate method for leak detection and localization of leakage suitable for Indian conditions.

Cattaneo-Christov Double Diffusion (CCDD) on Sutterby Nanofluid with Irreversibility Analysis and Motile Microbes Due to a RIGA Plate.

In this article, a Riga plate is exhibited with an electric magnetization actuator consisting of permanent magnets and electrodes assembled alternatively. This Riga plate creates an electric and magnetic field, where a transverse Lorentz force is generated that contributes to the flow along the plate. A new study field has been created by Sutterby nanofluid flows down the Riga plate, which is crucial to the creation of several industrial advancements, including thermal nuclear reactors, flow metres, and nuclear reactor design. This article addresses the second law analysis of MHD Sutter by nanofluid over a stretching sheet with the Riga plate. The Cattaneo–Christov Double Diffusion heat and mass flux have been created to examine the behaviour of relaxation time. The bioconvection of motile microorganisms and chemical reactions are taken into consideration. Similarity transformations are used to make the governing equations non-dimensional ordinary differential equations (ODE’s) that are subsequently solved through an efficient and powerful analytic technique, the homotopy analysis method (HAM). The effect of pertained variables on velocity, temperature, concentration, and motile microorganism distributions are elaborated through the plot in detail. Further, the velocity distribution enhances and reduces for greater value Deborah number and Reynold number for the two cases of pseudoplastic and dilatant flow. Microorganism distribution decreases with the augmented magnitude of Peclet number , Bioconvection Lewis number , and microorganism concentration difference number . The entropy production distribution is increased for the greater estimations of the Reynolds number and Brinkman parameter . Two sets of graphical outputs are presented for the Sutterby fluid parameter. Finally, for the justification of these outcomes, tables of comparison are made with various variables.

A Review of Arabic Intelligent Chatbots: Developments and Challenges

Chatbots, or machine based conversational systems, have become popular in recent years in a variety of applications. Chatbots are intelligent technologies that employ Artificial Intelligence (AI) to converse with humans in their natural languages. Chatbot's primary function is to interpret user's questions and respond with the most convenient intelligent and natural responses. In a number of the world's most generally spoken languages, chatbots appear to be quite successful; however, Arabic chatbots have not yet attained the predicted degree of success. Many academics have recently sought to bridge the gap in the implementation of Arabic chatbots by overcoming the complicated linguistic Arabic language features. This survey reviews the chatbots, their categories and implementation techniques. It also, presents some challenges that face the developer of Arabic chatbots due to rich complex properties of Arabic language. Then, most of published chatbot researches that deal with Arabic language are highlighted. Finally, a table of comparison among the mentioned Arabic chatbots is presented. The study concludes that, all the present AI based Arabic chatbots are developed in retrieval mode, and that Arabic chatbot literature is scarce in comparison to that in many other languages.

Różnice w podejściach do badania zaufania międzyorganizacyjnego w literaturze polskojęzycznej i anglojęzycznej

In recent years both in Polish and international literature, the number of studies devoted to trust in inter-organisational relations has grown at a steady pace. The authors of these studies tend to apply two methodological approaches: variance theory and process theory. The aim of this article is to compare Polish and English language academic literature with regard to the methodological approaches. The article is based on a systematic literature review of inter-organisational trust, with literature collected based on keywords and snowball methodology. The election was based on abstract analysis and the text of selected articles. The methodologies of the selected articles were presented and compared. From the initial pool of 726 positions, 32 articles in Polish and 30 in English were analysed and compared, with the data presented in a comparison table. The findings indicate a research gap in Polish-language literature in the form of an insufficient amount of research on trust at inter-organisational level using a process-based approach. Applying this approach would potentially deepen the understanding of organisations' relationships with each other, especially in the context of Poland.

Network Data Center Traffic Predictive Model Analysis Based on Machine Learning

Due to the transient nature and uncertainty of traffic produced by applications and services, data center networks have a lot of challenges. As a response, networking as a domain is continually evolving to maintain the exponential growth in network traffic. The primary objective of this paper is to predict the network traffic before it impacts the system's performance. This paper first describes existing Machine Learning (ML) applications in telecommunications and then lists the most prominent difficulties and probable remedies for implementing them. We tried to implement different ML algorithms to predict the network traffic like Gradient Boosting (GB), Random Forest (RF), K-Nearest Neighbor (KNN), Adaptive Boosting (AB), Neural Network (NN), Decision Tree (DT), and Support Vector Machines (SVM) with different sub-parameters for predicting network traffic. Relying on a sequential dataset, we create the corresponding ML environment and present a comparison table of Mean square error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) for each model. The simulation results show that the AB and GB are the best-fitted models with performance matrix parameters like MSE 0.000 and RMSE 0.002 and 0.011, respectively. The orange tool is used to stimulate the predictive models.

Integrated Lasers on Silicon at Communication Wavelength: A Progress Review

AbstractWith the emerging trend of big data and internet of things, silicon (Si) photonics technology has been developed and applied for high‐bandwidth data transmission. Contributed by the advantages of Si including high refractive index, low loss, significant thermal‐optical effect, and CMOS compatibility, various functional photonic devices have been demonstrated. One exception is the laser source, which is limited by the indirect bandgap of Si. Researchers have come up with different ways to make lasers on Si. This review presents the development progress of integrated laser sources on Si, with the focus on the wavelength regimes for communication. These lasers are categorized based on their gain media, including III–V semiconductor‐based laser, germanium/germanium tin‐based laser, Raman‐based laser, and rare‐earth‐doped laser. The laser performance and characteristics are summarized in tables for comparison. The key results are discussed. Future perspectives on the development of integrated lasers on Si have also been provided.

Analysis of machine learning methods in the task of searching duplicates in the software code

The object of the study is code in the Python programming language, analyzed by machine learning methods to identify clones. This work is devoted to the study of machine learning methods and implementation of the decision tree machine learning model in the problem of finding clones in the program code. The paper also analyzes existing machine learning approaches for detecting duplicates in program code. During the comparison, the advantages and disadvantages of each algorithm were determined, and the results were summarized in the corresponding comparison tables. As a result of the analysis, it was determined that the method based on the decision tree, which gives the best result in the task of finding clones in the program code, is the most optimal both from the point of view of accuracy and from the point of view of implementation. The result of the work is a created model that, with an accuracy of more than 99 %, classifies cloned and non-cloned codes on an automatically generated dataset in a minimal amount of time. This system has several open questions for future research, the list of which is presented in this work. The proposed model has the following ways of further development: – recognition of clones rewritten from one programming language to another; – detection of vulnerabilities in the code; – improvement of model performance by creating more universal datasets. The perspective of the work lies in training a decision tree model for accurate and fast detection of code clones, which can potentially be widely used for plagiarism detection in both educational institutions and IT companies.

Floating Nodes Assisted Cluster-Based Routing for Efficient Data Collection in Underwater Acoustic Sensor Networks

Exhaust research work has been carried out in the field of Terrestrial Wireless Sensor Networks (TWSN). Now, research penetration is moving toward Underwater Acoustic Sensor Networks (UASNs). The communication of UASNs has dependencies on acoustic instead of radio. Efficient data collection by the UASN seems a tedious job in comparison with TWSN due to the peculiar features of underwater communication. However, there is a way to accomplish efficient data collection metrics through the design of routing protocols by considering the unique features of UASN communications. In this context, we propose a novel scheme and we call it Floating Nodes assisted Cluster-Based Routing (FNCBR) scheme for efficient data collection in UASNs. In FNCBR, the network space is divided into cuboids to form clusters. Then, every cuboid is consigned with a floating node (FN) at the surface layer and two fixed cluster heads (CHs) are suspended at different depth levels. All CHs of cuboids are supposed to be connected with a floating node via a wired connection, while source nodes are haphazardly distributed in the whole network region. In FNCBR, source nodes are liable to send the sensed data either to the FN or to the nearest CH. The data collected by the CHs is moved towards the FNs, which further disseminate the data to the on-shore monitoring center via a Radio Frequency (RF) link. We conduct the simulations in Network Simulator (NS-3) to appraise the proposed FNCBR protocol with other clustering protocols (e.g., CVBF, BS-CVBF, and ANCRP) in terms of different performance metrics. After describing the results, a quantitative comparison table is also given. The FNCBR scheme is also evaluated in terms of multiple FN densities. The simulation outcomes justify that the proposed FNCBR scheme has given better results in all performance metrics than the baseline schemes.

An Electronically Tunable Meminductor Emulator and Its Application in Chaotic Oscillator and Adaptive Learning Circuit

In this paper, a new design of meminductor emulator has been suggested using voltage differencing buffered amplifier (VDBA), current differencing buffered amplifier (CDBA), a resistor and two grounded capacitors. The key advantages of the proposed meminductor emulator circuits are their design simplicity, implementation without memristors and easily adjustable design for grounded/ floating decremental to incremental configurations. The proposed circuits have been designed and simulated using 180-nm process technologies in the Mentor Graphics Eldo simulation tool. The nonvolatility and Monte Carlo analysis of the proposed meminductor emulators have been given. A comparison table has also been presented to discuss the benefits of the suggested meminductor emulator designs with existing designs. The operation of the designed emulator has been corroborated by using it in the chaotic oscillator and neuromorphic circuit applications.

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

Use of multilayer recursive model for non-linear dynamic system identification

In practice, the dynamics of the system are uncertain due to nonlinear and dynamic characteristics. It is difficult to establish accurate identification and prediction of the nonlinear plants that require dynamic modelling of the system. Extreme learning machine (ELM) as the recursive model due to its fast training and convergence speed is utilized in this work. However, its limitation is that it has only 1 hidden neuron which tends to make evolution speed low. Further, Multi-layer ELM (ML-ELM) model is applied on a nonlinear Auto-regressive complex benchmark system. The performance of ML-ELM is compared with dynamic recurrent functional link neural network (DRFLNN), functional link neural network (FLNN), nonlinear auto-regressive moving average (NARAX), multi-layer perception (MLP), radial basis function network (RBFN), Elman recurrent neural network (ERNN), and basic ELM models. From the comparison table, it can be seen that ML-ELM has better performance as compared with other models.

Robotics in laparoscopic surgery - A review

AbstractBecause of the increasing use of laparoscopic surgeries, robotic technologies have been developed to overcome the challenges these surgeries impose on surgeons. This paper presents an overview of the current state of surgical robots used in laparoscopic surgeries. Four main categories were discussed: handheld laparoscopic devices, laparoscope positioning robots, master–slave teleoperated systems with dedicated consoles, and robotic training systems. A generalized control block diagram is developed to demonstrate the general control scheme for each category of surgical robots. In order to review these robotic technologies, related published works were investigated and discussed. Detailed discussions and comparison tables are presented to compare their effectiveness in laparoscopic surgeries. Each of these technologies has proved to be beneficial in laparoscopic surgeries.

Refractive index sensor with magnified resonant signal

Herein, we theoretically suggest one-dimensional photonic crystal composed of polymer doped with quantum dots and porous silicon. The present simulated design is proposed as a refractive index biosensor structure based on parity-time symmetry. Under the parity-time conditions, the transmittance of the resonant peaks is magnified to be 57,843% for refractive index 1.350, 2725% for 1.390, 2117% for 1.392, 1502% for 1.395, 1011% for 1.399, and 847% for 1.401. By magnification, we can distinguish between different refractive indices. The present design can record an efficiency twice the published designs as clear in the comparison table. Results clear that the sensitivities are 635 nm/RIU and 1,000,000%/RIU. So, it can be used for a broader range of detection purposes.

Adversarial Training Methods for Deep Learning: A Systematic Review

Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the threat of adversarial attacks. It is a training schema that utilizes an alternative objective function to provide model generalization for both adversarial data and clean data. In this systematic review, we focus particularly on adversarial training as a method of improving the defensive capacities and robustness of machine learning models. Specifically, we focus on adversarial sample accessibility through adversarial sample generation methods. The purpose of this systematic review is to survey state-of-the-art adversarial training and robust optimization methods to identify the research gaps within this field of applications. The literature search was conducted using Engineering Village (Engineering Village is an engineering literature search tool, which provides access to 14 engineering literature and patent databases), where we collected 238 related papers. The papers were filtered according to defined inclusion and exclusion criteria, and information was extracted from these papers according to a defined strategy. A total of 78 papers published between 2016 and 2021 were selected. Data were extracted and categorized using a defined strategy, and bar plots and comparison tables were used to show the data distribution. The findings of this review indicate that there are limitations to adversarial training methods and robust optimization. The most common problems are related to data generalization and overfitting.