Internet of Things based Messaging Protocols for Aquaculture Applications - A Bibliometric Analysis and Review

Internet of Things (IoT) which connects real-world physical objects with various identities involves different technologies and research areas. As it is an integration of different standards and technologies with numerous capabilities, the implementation phase needs to consider important parameters of communication. In IoT this is achieved through messaging protocols. Each object has its own limitations in terms of sensing capability, storage capacity, connectivity, power utilization, etc. And hence when such objects are deployed for different applications, they need to perform well in terms of their various capabilities. Messaging protocols at this stage need to consider these diverse elements. One of such IoT enabling technologies can be categorized as communication technology and networks, wherein data transmission protocols such as Hypertext Transmission Protocol (HTTP), Constrained Application Protocol (CoAP), Message Queue Telemetry Protocol (MQTT), MQTT for Sensor Networks (MQTT-SN), Advanced Message Queuing Protocol (AMQP) are used for data transmission. Each protocol has its own messaging architecture and standard. Any IoT application intends to provide optimum utilization of limited processing power and energy. In such a scenario integration and translation between various popular messaging protocols is needed. In this article bibliometric study for application like Aquaculture has been undertaken. The analysis done through Scopus database provides information about prominent countries involved in research field, highest citation documents, co-authorship links, funding sponsors etc. The bibliometric study conducted helped in understanding scope of the research field

Bibliometric Analysis of Firefly Algorithm Applications in the Field of Wireless Sensor Networks

Wireless Sensor Network is a network of wireless sensor nodes that are capable of sensing information from their surroundings and transmit the sensed information to data collection point known as a base station. Applications of wireless sensor networks are large in number and forest fire detection, landslide monitoring, etc. are few applications to note. The research challenges in wireless sensor networks is the transmission of data from the sensor node to the base station in an energy-efficient manner and network life prolongation. Cluster-based routing techniques are extensively adopted to address this research challenge. Researchers have used different metaheuristic and soft computing techniques for designing such energy-efficient routing techniques. In the literature, a lot of survey article on cluster-based routing methods are available, but there is no bibliometric analysis conducted so far. Hence in this research article, bibliometric study with the focus on the firefly algorithm and its applications in wireless sensor network is undertaken. The purpose of this article is to explore the nature of research conducted concerning to authors, the connection between keywords, the importance of journals and scope for further research in soft computing based clustered routing methods. A detailed bibliometric analysis is carried out by collecting the details of published articles from the Scopus database. In this article, the collected data is articulated in terms of yearly document statistics, key affiliations of authors, contributing geographical locations, subject area statistics, author-keyword mapping, and many more essential aspects of bibliometric analysis. The conducted study helped in understanding that there is a vast scope for the research community to perform research work concerning firefly algorithm applications in the field of wireless sensor networks

Bibliometric of Feature Selection Using Optimization Techniques in Healthcare using Scopus and Web of Science Databases

Feature selection technique is an important step in the prediction and classification process, primarily in data mining related aspects or related to medical field. Feature selection is immersive with the errand of choosing a subset of applicable features that could be utilized in developing a prototype. Medical datasets are huge in size; hence some effective optimization techniques are required to produce accurate results. Optimization algorithms are a critical function in medical data mining particularly in identifying diseases since it offers excellent effectiveness in minimum computational expense and time. The classification algorithms also produce superior outcomes when an objective function is built using the feature selection algorithm. The solitary motive of the research paper analysis is to comprehend the reach and utility of optimization algorithms such as the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO) in the field of Health care. The aim is to bring efficiency and maximum optimization in the health care sector using the vast information that is already available related to these fields. With the help of data sets that are available in the health care analysis, our focus is to extract the most important features using optimization techniques and work on different algorithms so as to get the most optimized result. Precision largely depends on usefulness of features that are taken into consideration along with finding useful patterns in those features to characterize the main problem. The Performance of the optimized algorithm finds the overall optimum with less function evaluation. The principle target of this examination is to optimize feature selection technique to bring an optimized and efficient model to cater to various health issues. In this research paper, to do bibliometric analysis Scopus and Web of Science databases are used. This bibliometric analysis considers important keywords, datasets, significance of the considered research papers. It also gives details about types, sources of publications, yearly publication trends, significant countries from Scopus and Web of Science. Also, it captures details about co-appearing keywords, authors, source titles through networked diagrams. In a way, this research paper can be useful to researchers who want to contribute in the area of feature selection and optimization in healthcare. From this research paper it is observed that there is a lot scope for research for the considered research area. This kind of research will also be helpful for analyzing pandemic scenarios like COVID-19

Bibliometric Analysis of Particle Swarm Optimization Techniques used to enhance Low-Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Networks

Wireless Sensor Network (WSN) is a network of tiny wireless sensor nodes. The sensor nodes sense information and transmit the sensed information to a data collection point known as Base Station. WSNs have gained massive popularity due to their incredible benefits, and active research is ongoing for the past two decades. The primary concern with WSN is that the sensor operates on a limited power supply. Due to the nature of applications of WSNs and the hostile environment where the sensors are deployed, providing unlimited power or energy supply is not an option. Hence, the research work mainly focuses on energy efficiency and network life prolongation so that WSN can operate for a longer duration. Design and development of energy-efficient routing protocols is an active research field undertaken in WSNs. Low-Energy Adaptive Clustering Hierarchy Protocol (LEACH) is one of the most cited and referred cluster-based routing protocol in the field of WSN. Many research articles showcase novel methods to improve LEACH protocol\u27s performance, and Particle Swarm Optimization (PSO), a widespread nature-inspired optimization technique, has been extensively applied to improve LEACH. This bibliometric research article aims to know the pattern of PSO techniques used in LEACH improvement and understand the relationships among researchers, authors, published documents, sources, keywords, funding agencies, etc. This article has projected a detailed bibliometric analysis of PSO techniques for LEACH protocol by querying the Scopus Database. The data collected is articulated in subject areas, top authors and sources of documents, co-citation analysis, keyword co-occurrence analysis, etc. For conducting the bibliometric analysis, some well-known tools such as ScienceScape and VosViewer are utilized. The study revealed that there are opportunities to extend the research work in WSN and cluster-based routing methods

Estimating Remaining Useful Life in Machines Using Artificial Intelligence: A Scoping Review

The remaining useful life (RUL) estimations become one of the most essential aspects of predictive maintenance (PdM) in the era of industry 4.0. Predictive maintenance aims to minimize the downtime of machines or process, decreases maintenance costs, and increases the productivity of industries. The primary objective of this bibliometric paper is to understand the scope of literature available related to RUL prediction. Scopus database is used to perform the analysis of 1673 extracted scientific literature from the year 1985 to 2020. Based on available published documents, analysis is done on the year-wise publication data, document types, language-wise distribution of documents, funding sponsors, authors contributions, affiliations, document wise citations, etc. to give an in-depth view of the research trends in the area of RUL prediction. The paper also focuses on the available maintenance methods, predictive maintenance models, RUL models, deep learning algorithms for RUL prediction challenges and future directions in the RUL prediction area

Breast Cancer Detection from Histopathology Images using Machine Learning Techniques: A Bibliometric Analysis

Computer aided diagnosis has become upcoming area of research over past few years. With the advent of machine learning and especially deep learning techniques, the scenario of work flow management in healthcare sector is changing drastically. Artificial intelligence has shown potential in the field of breast cancer care. With datasets for machine learning frameworks getting eventually richer with time, we can definitely get newer insights in the field of breast cancer care. This will help in narrowing down the treatment range for patients and increasing patient survivability. The purpose of this study was to perform bibliometric analysis of the literature in the area of breast cancer detection using machine learning. Analysis was done for various elements like publication types, highly influential authors, most prominent journals, institutional affiliations, main keywords, etc. This analysis may direct future researchers by giving thorough quantitative evaluation of research documents in the field of breast cancer detection using machine learning

Extraction and Performance Analysis of Hydrocarbons from Waste Plastic Using the Pyrolysis Process

Ecosystem destruction is one of today’s significant challenges due to fast industrialisation and an increasing population. It takes several years for solid trash, such as plastic bottles and super-market bags, to decompose in nature. In addition, plastic disposal techniques such as landfilling, reuse, and incineration pose significant threats to human health and the environment. In this paper, we investigated whether the impact of mixing biodiesel with waste oil from recycled plastic on the resulting fuel mixture’s yields better physical and chemical properties. Consequently, pyrolysis is one of the most advantageous and practical waste disposal methods as it is both environmentally benign and efficient. Pyrolysis is the high-temperature thermal breakdown of solid waste to produce pyrolytic oil. The pyrolytic (plastic) oil produced is converted to a hydrocarbon-rich pyrolytic fuel. Similar to diesel and gasoline, pyrolytic fuel has the same calorific value. Internal combustion engines may operate on pyrolytic fuel without suffering a performance reduction. Researchers examined engine performance and exhaust pollutants. The research discovered that the engine could operate on plastic pyrolysis fuel at full load, enhance brake thermal efficiency by 6–8%, and lower UBHC and CO emissions; however, nitrous oxide (NOx) emissions were noticeably higher. The findings demonstrated the possibility of using plastic pyrolysis fuel as a diesel substitute

Three Degrees of Freedom Robotic arm and its Digital Twin using Simulink – A Bibliometric Analysis

The 3-degree Digital Twin robotic arm for freedom can diagnose the joints off-board by placing a torque in the robotic arm joint. MATLAB, Simulink®, SimscapeTM, and Simscape Multibody were the basis of this model. Virtual space with a virtual robot arm was connected to a physical space that was a 3D printed replica of the virtual space and robot arm, built using Unity (a modern Game Engine). The arm in the Digital Twin model was created using the hardware prototype’s dimensions, which were then used to simulate a real-world situation. With its revolute joints, the arm has a certain degree of freedom. The relations between the different elements of the arm were explained through frame transformations. Simulink signal builder generates the inputs. Robotic arms are one of the most frequently used devices for manufacturing processes and can automate a range of tasks on the floor, for instance. However, for these devices to work, they must have AI that has been properly trained. It proposes a method for dealing with virtual learning to the real robot twin mapping. Considering the importance of Digital Twin, it is essential to study the research trends and research origin. Hence, in this article detailed and meticulous study of digital twin and its bibliometric analysis is carried out. Various aspects such as top research articles, funding resources, affiliations, document types, etc. are collected from the Scopus database. The bibliometric study is even extended to network analysis to understand co-author relations, and citation analysis is also presented

Extraction and Performance Analysis of Hydrocarbons from Waste Plastic Using the Pyrolysis Process

Ecosystem destruction is one of today’s significant challenges due to fast industrialisation and an increasing population. It takes several years for solid trash, such as plastic bottles and super-market bags, to decompose in nature. In addition, plastic disposal techniques such as landfilling, reuse, and incineration pose significant threats to human health and the environment. In this paper, we investigated whether the impact of mixing biodiesel with waste oil from recycled plastic on the resulting fuel mixture’s yields better physical and chemical properties. Consequently, pyrolysis is one of the most advantageous and practical waste disposal methods as it is both environmentally benign and efficient. Pyrolysis is the high-temperature thermal breakdown of solid waste to produce pyrolytic oil. The pyrolytic (plastic) oil produced is converted to a hydrocarbon-rich pyrolytic fuel. Similar to diesel and gasoline, pyrolytic fuel has the same calorific value. Internal combustion engines may operate on pyrolytic fuel without suffering a performance reduction. Researchers examined engine performance and exhaust pollutants. The research discovered that the engine could operate on plastic pyrolysis fuel at full load, enhance brake thermal efficiency by 6–8%, and lower UBHC and CO emissions; however, nitrous oxide (NOx) emissions were noticeably higher. The findings demonstrated the possibility of using plastic pyrolysis fuel as a diesel substitute