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

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

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