International Journal on Recent and Innovation Trends in Computing and Communication
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Research on the Performance Management of Chinese University Football Teams: Based on the Qualitative Perspective of Coaches
Get PDFPurpose: The purpose of this study is to explore the performance management of Chinese university football teams.
Methods: We used the explanatory research paradigm to conduct in-depth interviews with 15 Chinese college football team coaches and administrators, which was the main source of empirical data.
Results: Coaches and administrators gave a holistic description of the performance planning, implementation and evaluation of college sports teams. They found that class management affects the systematic development of football team training; there is a contradiction between learning and training; the competition management is not in place; a lack of funds, medical care, etc.
Conclusion: The results show that using the balanced scorecard method to evaluate the performance management of university sports teams can improve the performance management of sports teams, this includes listening to the voice of the players, meeting the needs of the players, and focusing on the health, welfare and sustainable performance of the players
Review and Analysis of Product Review Sentiment Analysis using Improved Machine Learning Techniques
Get PDFSentiment analysis has emerged as a crucial task in the era of big data and social media. Understanding the sentiments expressed in product reviews is vital for businesses to gauge customer satisfaction and make informed decisions. This research paper presents a design simulation and assessment of product review sentiment analysis using improved machine learning techniques. The aim is to develop a robust sentiment analysis model that outperforms existing approaches in accuracy and efficiency. We propose a novel methodology that combines advanced feature extraction, sentiment classification algorithms, and model optimization techniques.The introduction provides an overview of the importance of sentiment analysis in the context of product reviews and the challenges faced by conventional methods. It also outlines the objectives and scope of this research. The related works section presents a comprehensive review of existing literature and highlights the limitations of current approaches. The proposed methodology section describes the technical details of our enhanced machine learning approach and the reasoning behind the selected techniques.In the analysis of sample results, we evaluate the performance of our proposed model on a diverse dataset of product reviews. We present the accuracy, precision, recall, and F1-score metrics, along with a comparison to baseline models and state-of-the-art sentiment analysis systems. Furthermore, we discuss the model's robustness in handling various types of products and reviews.
Our research demonstrates significant improvements in sentiment analysis accuracy compared to traditional methods. We introduce tables and graphs to illustrate the model's performance in different scenarios and identify its strengths and weaknesses. The paper concludes by discussing the implications of our findings, potential applications in industry, and directions for future research. Overall, this research contributes to the advancement of sentiment analysis techniques and provides a valuable resource for businesses aiming to enhance their understanding of customer sentiments through product reviews
Solar Powered Mobile Charging Station
Get PDFWith the growing popularity of mobile electronics, charging solutions that are both affordable and environmental friendly are necessary.
This article explores the development and deployment of solar-powered mobile charging stations as an environmentally friendly and efficient way to meet this growing demand.
The charging station uses solar energy through photovoltaic panels, providing a clean and renewable energy source.
The model integrates modern technologies such as energy- efficient batteries, intelligent charging controls, and user- friendly interfaces to maximize the efficiency of charging performance and enhance user experience.
The main components of solar powered mobile charging stations include high-efficiency solar panels, energy storage systems, charging ports compatible with mobiles.
With its modular design, the station can be situated in various locations, including city centers, public squares, and remote areas that have limited access to the traditional power grid.
As discussed, the challenges and opportunities associated with the widespread adoption of solar charging infrastructure, including considerations for technology integration, maintenance, and public awareness.
The further future Scope or steps that can be taken to enhance the project are:-
Our main focus -To make our solar panel model rotate in an automatic manner where the intensity of sunlight is
Technological Advancement -Like increasing the efficiency and energy storage capacity
Market Growth -it will help in increasing the adoption of this technology, it will help in development of rural and urban areas, it will help in increasing the business
It will help in increasing the sustainability which will have a positive impact on ou
Leadership in AI-Driven Data Science: Fostering Innovation and Collaboration for Advancing Healthcare
Get PDFThis study investigates the application of AI algorithms in the healthcare industry, namely CNNs, RNNs, SVMs, and RFs. It assesses algorithm performance and applications and talks about the role of leadership in AI-driven data science. SVMs do well in classification, RFs in decision-making, RNNs in sequential data, and CNNs in medical imaging. Leadership qualities such as technical expertise and moral discernment are essential. Deep learning developments as well as blockchain and IoMT integration are all part of the future scope
Three Phase Grid Connected Solar PV System Modelling for Micro Grid
Get PDFTo address the problems of existing grid systems, the new micro grid idea provides a superior alternative by allowing for the incorporation of sustainable power. The biggest issue with renewable energy generation is that it is intermittent and depends on the weather. This nature resulted in substantial voltage swings. Rapid adjustment is becoming increasingly important for energy transmission and distribution networks. Already, numerous solutions have been described to address the challenges that are limited to a single transmission line. However, for multi-line power flow regulation, a new type of device known as GIPFC was implemented for the hybrid micro grid. As a step-by-step approach for implementing the respective suggested system, all of the various hybrid micro grid elements are first modelled, and the proposed system is then implemented at the grid connected end. The paper's key contribution was to simulate the PV system in the MATLAB/Simulink environment to analyze the three phase outputs
Applicability Assurance of wireless Sensors in Agricultural Process Management Using Online Remote Laboratory
Get PDFThe paper will provide proof of the efficacy of remote sensors in agriculture as a process control approach using an online laboratory. Data of environmental parameter, such as temperature, humidity, soil moisture and light intensity, were gathered from sensor node system which had been installed in open land of agricultural field. Four machine learning models including ours have been put into going forward and back to the future of agriculture. These are the Simple Linear Regression, Decision Tree, k-Nearest Neighbors, and Support Vector Machine that have been established and examined for their efficacy in predicting and managing agriculture processes using this data you provided. The findings indicated that the Decision Tree method qualified for the best 92% of the accuracy index, while Support Vector Machine produced the accuracy of 90% in their outcome. Neural Network algorithm showed an 88% accuracy, while Simple Linear Regression algorithm trailed with 85% accuracy The result signifies the fact that computer learning software, such as tree of decisions and support schemes can highly be used in improvement of agriculture systems through real-time control and responses. The combining of online remote experiments serves to create a scalable and affordable platform on which agricultural scientists and specialists can work together and make progress in agricultural technology which supports the advancement of more efficient and sustainable food production systems
Machine Learning Methodologies Based Improved Classification System for Sentiment Analysis of Tweets
Get PDFAn increasingly important part of studying public opinion, sentiment patterns, and how people see brands is analyzing tweets for sentiment. The need for effective and precise sentiment analysis techniques is growing in tandem with the volume of social media data. This article details an extensive investigation into the planning, modeling, and evaluation of an enhanced machine learning approach to sentiment analysis of tweets. In order to achieve better results in sentiment classification, the suggested approach integrates the best of natural language processing methods with state-of-the-art machine learning algorithms. The paper begins by outlining the relevance and uses of sentiment analysis in different fields. It draws attention to the necessity for more reliable and precise methodology by discussing the problems with conventional sentiment analysis techniques. After that, the article dives into related research, looking at current state-of-the-art methods and finding holes that the suggested approach intends to fill. The methodology part explains how the sentiment analysis pipeline works. Tokenization, stop-word removal, and stemming are part of the data preparation steps that start it all. Word embeddings and TF-IDF are two of the feature extractions approaches that are investigated and contrasted. An enhanced machine learning algorithm integrating deep learning and ensemble learning is subsequently introduced in the article. The results show that the suggested methodology achieves better accuracy and resilience in sentiment classification than traditional sentiment analysis approaches, and it also elaborates on the model's architecture, training process, and strategies for optimizing performance parameters. The article emphasizes the model's capabilities in dealing with sentiment analysis problems such as context-specific language, sarcasm, and irony. Its capacity to manage massive datasets in real-time further demonstrates the efficacy of the suggested technique. This study article concludes by stressing the significance of sentiment analysis in gaining insight into public opinion and its function in governmental and corporate decision-making. Results from using the suggested methods to analyze the sentiment of tweets and other social media data are encouraging. In its last section, the paper proposes avenues for additional investigation into how to improve sentiment analysis methods and deal with new problems that are cropping up in the industry
Integrating Probabilistic and Fuzzy Logic for Enhanced Natural Language Semantics Interpretation
Get PDFNatural language semantics interpretation is key to AI and computational linguistics growth. Traditional methods struggle with human language's ambiguity and imprecision, making text reading, sentiment analysis, and machine translation difficult. This research innovates by combining probabilistic and fuzzy logic to address natural language's vagueness and uncertainty. We present a probabilistic language semantics architecture that uses fuzzy logic to handle linguistic nuances and gradable categories. We start by building a probabilistic model to assess uncertainty and forecast corpus semantic links. Fuzzy logic is then used to interpret non-binary degrees of truth and conceptual boundaries. In various semantic interpretation tasks, this hybrid model outperforms existing techniques and captures a more sophisticated comprehension of natural language. Our model's adaptability and exceptional performance on datasets from many domains set a new benchmark for natural language semantics interpretation. Our study enables more intuitive and human-like language processing systems, which has broad implications for theoretical linguistics and AI applications
Unified Modelling Language (UML) Tool for Software
Get PDFThe production of software has going to move a lot much beyond the traditional development of software, described as an interactive autonomous software component by the structured programming paradigm of the late sixties and early 1970s. The study seeks to identify a UML profile, outline software projects in which UML was used to evaluate the use and efficiency of UML diagrams, determine the use of CASE tools and record the perceived use of UML language. A study survey was developed for IT professionals and university students. The research was conducted. There have been mailing lists. The findings indicate that UML is used in most software development projects successfully and that many of the users consider UML to be good since it helps to build the system more rapidly and to produce excellent software systems. For performance risk assessment, UML diagrams are used, a software model is created for each scenario, and are translated into a system execution model through deployment data
Improving QoS by SDN based Handover Management in 5G Networks
Get PDFThe purpose of this article is to enhance handover procedure in small cell based Ultra-Dense 5G Networks. The large number of small cells in dense heterogeneous 5G networks may result in unnecessary, frequent, and back and forth handovers with additional problems related to increased delay and total failure of handover process. Additionally, due to the separation of control and data signaling in 5G technology, the handover operation must be executed in both tiers. In this article we propose an SDN based approach to enhance handover mechanism.
The simulated results for E2E delay and throughput are compared with mininet based emulator The proposed strategy reduces the handover delay and failures by 36 and 24 percent respectively