Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

A new spatial spherical pattern model into interactive cartography pattern: multi-dimensional data via geostrategic cluster

A growing amount of research conducted in digital, cooperative with advances in Artificial Intelligence, Computer Vision including Machine learning, has managed to the advance of progressive techniques that aim to detect and process affective information contained in multi-modal evidences. This research intends to bring together for theoreticians and practitioners from academic fields, professionals and industries and extends to be visualizing cries such epidemic, votes, social Phenomena in spherical representation interactive model working in the broad range of topics relevant to multi - modal data processing and forensics tools developing. Furthermore, progress has been made in this research besides that in this research conducted progression of mapping claims in present epoch necessitate the capacities of virtual guide of any understandable Geo-Visualization of spatial features that talented to convert the quantities of spatial pattern into cartography. The enlargement of a novel approaches fit for visualization of spatial pattern constituencies Starting exclusive Input Set of object O, set associated with feature F for regenerating Output the set C , interested region I special target C Even so, as indicated by the construction of the prototype as listed earlier in this thread, does it have the incentive for improvements: Representation could be used by Google Earth can Using Project enhancement representation whereby provides a 3D or 4D interaction with life measures with a view to cartography. In addition, the initiative suggests that a tool not accessible for disseminating information to the public can be addressed by the use of online mapping, which fuses with trends visualization for political circles and electors. But as mentioned above the framework is developed and it's also possible in the current example, for improvements: The project's representation 3D or 4D interacting Earth can use measures of life Earth From the map viewpoint. That's what that says. That means that. Which just means. Developers have concerns that. So it. Designers concern about that. This study supports the new, multi - demission and deployed countries in conjunction with another data is processed. Comprehensive, well-interpreted source data for the Data like Malaysia Jabatan Pendaftaran (JPN).

     

Role of Fuzzy Approach towards Fault Detection for Distributed Components

Component-based software development is rapidly introducing numerous new paradigms and possibilities to deliver highly customized software in a distributed environment. Among other communication, teamwork, and coordination problems in global software development, the detection of faults is seen as the key challenge. Thus, there is a need to ensure the reliability of component-based applications requirements. Distributed device detection faults applied to tracked components from various sources and failed to keep track of all the large number of components from different locations. In this study, we propose an approach for fault detection from component-based systems requirements using the fuzzy logic approach and historical information during acceptance testing. This approach identified error-prone components selection for test case extraction and for prioritization of test cases to validate components in acceptance testing. For the evaluation, we used empirical study, and results depicted that the proposed approach significantly outperforms in component selection and acceptance testing. The comparison to the conventional procedures, i.e., requirement criteria, and communication coverage criteria without irrelevancy and redundancy successfully outperform other procedures. Consequently, the F-measures of the proposed approach define the accurate selection of components, and faults identification increases in components using the proposed approach were higher (i.e., more than 80 percent) than requirement criteria, and code coverage criteria procedures (i.e., less than 80 percent), respectively. Similarly, the rate of fault detection in the proposed approach increases, i.e., 92.80 compared to existing methods i.e., less than 80 percent. The proposed approach will provide a comprehensive guideline and roadmap for practitioners and researchers.     

DEICA: A differential evolution-based improved clustering algorithm for IoT-based heterogeneous wireless sensor networks

With the evolution of technology, many modern applications like habitat monitoring, environmental monitoring, disaster prediction and management, and telehealth care have been proposed on wireless sensor networks (WSNs) with Internet of Things (IoT) integration. However, the performance of these networks is restricted because of the various constraints imposed due to the participating sensor nodes, such as nonreplaceable limited power units, constrained computation, and limited storage. Power limitation is the most severe among these restrictions. Hence, the researchers have sought schemes enabling energy-efficient network operations as the most crucial issue. A metaheuristic clustering scheme is proposed here to address this problem, which employs the differential evolution (DE) technique as a tool. The proposed scheme achieves improved network performance via the formulation of load-balanced clusters, resulting in a more scalable and adaptable network. The proposed scheme considers multiple parameters such as nodes' energy level, degree, proximity, and population for suitable network partitioning. Through various simulation results and experimentation, it establishes its efficacy over state-of-the-art schemes in respect of load-balanced cluster formation, improved network lifetime, network resource utilization, and network throughput. The proposed scheme ensures up to 57.69%, 33.16%, and 57.74% gains in network lifetime, energy utilization, and data packet delivery under varying network configurations. Besides providing the quantitative analysis, a detailed statistical analysis has also been performed that describes the acceptability of the proposed scheme under different network configurations.     

The effect of infrared laser radiation of different frequencies on the healing of skin wounds

In experiments on white rat males it was established that infrared laser radiation (frequency 500 Hz) was more potent in stimulating repair of skin wounds than 3000 Hz radiation. This manifested in enhanced proliferative activity of fibroblasts and epithelial cells, decreased areas of the wound.     

Validation of caregiver interviews to diagnose common causes of severe neonatal illness

The objective of this study was to validate retrospective caregiver interviews for diagnosing major causes of severe neonatal illness and death. A convenience sample of 149 infants aged < 28 days with one or more suspected diagnoses of interest (low birthweight/severe malnutrition, preterm birth, birth asphyxia, birth trauma, neonatal tetanus, pneumonia, meningitis, septicaemia, diarrhoea, congenital malformation or injury) was taken from patients admitted to two hospitals in Dhaka, Bangladesh. Study paediatricians performed a standardised history and physical examination and ordered laboratory and radiographic tests according to study criteria. With a median interval of 64.5 days after death or hospital discharge, caregivers of 118 (79%) infants were interviewed about their child's illness. Using reference diagnoses based on predefined clinical and laboratory criteria, the sensitivity and specificity of particular combinations of signs (algorithms) reported by the caregivers were ascertained. Sufficient numbers of children with five reference standard diagnoses were studied to validate caregiver reports. Algorithms with sensitivity and specificity > 80% were identified for neonatal tetanus, low birthweight/severe malnutrition and preterm delivery. Algorithms with specificities > 80% for birth asphyxia and pneumonia had sensitivities < 70%, or alternatively had high sensitivity with lower specificity. In settings with limited access to medical care, retrospective caregiver interviews provide a valid means of diagnosing several of the most common causes of severe neonatal illness and death.     

Adolescent substance abuse: a review of the past 10 years

VTG was purified from seabream Sparus aurata plasma by ion exchange chromatography on a DEAE-Sepharose column. The vitellogenin was characterized and its properties were determined. The molecular mass of the native form, obtained by Sephadex G-200 column, was around 450 kDa, whereas an apparent molecular mass of 180 kDa was detected by electrophoresis under denaturing and reducing conditions, suggesting a dimeric form for the native protein. The presence of carbohydrates was determined using concanavalin A, while the presence of phosphate groups was detected by Stains-all, a cationic stain. These data together with the sex specificity, the estrogen inducibility, and the cross-reactivity of the abVTG against the major yolk proteins identifies this protein as vitellogenin. The validated ELISA was used for a rapid and reliable measurement of plasma VTG changes related with those of estradiol-17beta in female broodstock.     

FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.     

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases. Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure. Although various approaches for retinal vessel segmentation are extensively utilized, however, the responses are lower at vessel's edges. The curvelet transform signifies edges better than wavelets, and hence convenient for multiscale edge enhancement. The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges. Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges. Therefore, in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image. Afterwards C mean thresholding is used for the extraction of vessel. The recommended fusion approach is assessed on DRIVE dataset. Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result. The results demonstrate that the recommended method outperforms the traditional approaches.     

A Secure Communication Protocol for Unmanned Aerial Vehicles

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles. Multiple UAVs and autopilot systems support it, and it provides bi-directional communication between the UAV and Ground Control Station. The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS. In order to increase the transfer speed and efficiency, the Mavlink does not encrypt the messages. As a result, the protocol is vulnerable to various security attacks such as Eavesdropping, GPS Spoofing, and DDoS. In this study, we tackle the problem and secure the Mavlink communication protocol. By leveraging the Mavlink packet’s vulnerabilities, this research work introduces an experiment in which, first, the Mavlink packets are compromised in terms of security requirements based on our threat model. The results show that the protocol is insecure and the attacks carried out are successful. To overcome Mavlink security, an additional security layer is added to encrypt and secure the protocol. An encryption technique is proposed that makes the communication between the UAV and GCS secure. The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency. The results are validated in terms of transfer speed, performance, and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol. Our achieved results have significant improvement over the literature and Mavlink in terms of security.