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Diabetes Risk Prediction Through Fine-Tuned Gradient Boosting 13th

Diabetes Risk Prediction Through Fine-Tuned Gradient Boostin...

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13th international Advanced computing conference, IACC 2023

作者： Rani, Pooja Lamba, Rohit Sachdeva, Ravi Kumar Jain, Anurag Choudhury, Tanupriya Kotecha, Ketan Haryana Ambala Mullana India Haryana Ambala Mullana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Sciences University of Petroleum and Energy Studies Dehradun India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Pune412115 India

ISBN: (纸本)9783031567025

Diabetes, a chronic metabolic disease with a rising global prevalence, significantly impacts individuals’ health. Diabetes increases a person’s risk of developing various diseases, including heart disease, stroke, vision problems, nerve damage, etc. Early detection and proactive care of diabetes can lessen its impact and improve patient outcomes. Utilizing the powers of machine learning algorithms in the medical field has shown significant promise in accurately identifying diseases and implementing customized treatments, reducing the workload of healthcare professionals. This paper proposes a methodology based on Gradient Boosting technique to accurately predict diabetes. This study also provides a thorough analysis of diabetes prediction using a variety of classifiers, including Linear Discriminant Analysis (LDA), Extra Tree Classifiers (ETC), Quadratic Discriminant Analysis (QDA), Stochastic Gradient Descent (SGD), Bayesian Gradient Descent Classifiers (BGC), and Gradient Boosting (GB) classifiers. The pre-processing methods of Standard Scalar Normalization and Synthetic Minority Over-sampling Technique (SMOTE) are used to improve the predictive models’ quality. SMOTE is used for class balancing. Accuracies achieved by LDA, ETC, QDA, SGD, BGC, and GB are 77.34%, 74.20%, 73.50%, 75.01%, 74.08%, and 80.19%, respectively. The authors optimized the Gradient Boosting (GB) classifier through a rigorous grid search optimization process to maximize performance, yielding an accuracy of 82.70%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Discriminant analysis

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Development of Student Biochemical Index Monitoring System Based on K-means Cluster Analysis

Development of Student Biochemical Index Monitoring System B...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Chunxin Wang School of Physical Education Bohai University Jinzhou Liaoning China

data mining is a new type of information processing technology, through the analysis of massive data, to find hidden information, knowledge and trends. Cluster analysis is the most important data mining technology. The K-means algorithm is one of the more classic clustering algorithms, which optimizes the clustering results through step-by-step iteration. It has the advantages of reliable theory, simple implementation and fast convergence. Based on K-means clustering analysis method, this paper constructs a student biochemical index monitoring system, which provides reference for the formulation of students' physical health and development planning. The core work of this study is to build the mathematical model of K-means clustering analysis algorithm, design the conceptual structure and logical structure of the database, and complete the design of the student biochemical index cluster analysis software.

关键词： Software algorithms Clustering algorithms Information processing Market research Software Indexes data mining

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Application of Improved PilotNet Intelligent Traffic Algorithm Based on Baidu PaddlePaddle

Application of Improved PilotNet Intelligent Traffic Algorit...

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IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Huifang He Tingting Zhuang School of Information Engineering Guangdong Engineering Polytechnic Guangzhou China School of Data Science and Engineering Xingzhi College South China Normal University Shanwei China

In view of the complexity of current traffic scenes, the rapid development of intelligent transportation applications and the improvement of domestic platforms, this paper combines the current mainstream deep learning algorithms to recognize the road line with the PilotNet model improved by BatchNorm and Dropout technologies under the Baidu PaddlePaddle framework, and to detect the traffic signs with the YOLOv3 Tiny model. Due to the limited hardware system resources and the real-time nature of automatic driving, finally, the two types of models are deployed to the Edge Board computing box. In the application of intelligent transportation, single-thread technology cannot respond to the real-time nature of automatic driving, and thread blocking has a great impact. After comparison, dual-thread technology is adopted. data processing is carried out by capturing the moving image of the car and the control value, and the experiment is completed. In the experiments, the smart car can recognize the traffic signs at a speed of around 0.01s per frame and finish model loading and making decision after the recognition of traffic scenes in 34.86s, which shows that the trained neural network models all have lightweight characteristics and can meet the system application with limited resources.

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Automation of data Transmission for Wallet With Novel Merchant Payment System Using Cluster Classification Over Sequence Analysis Method

Automation of Data Transmission for Wallet With Novel Mercha...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Aishwary Awasthi Richa Mehta Mohit Rathod Hayder Mahmood Salman Bechoo Lal Badria SulaimanAlfurhood Department of Mechanical Engineering Sanskriti University Mathura Uttar Pradesh India Department of ECE Chandigarh Engineering College Jhanjeri India Department of CSE PIT Parul University Vadodara Gujarat India Computer Science Department Al-TurathUniversiy College Baghdad Iraq Department of CSE KoneruLakshmaiah Education Foundation (KLEF) KL Deemed University Vijayawada Campus AP India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia

To improvise a data transmission for wallet with merchant payment system using cluster classification over sequence analysis method is proposed in this paper. The implementation have been carried out for processing the data through MySQL and the algorithms are tested with Net Beans (IDE) applications. The cluster classification paved the way to achieve the enhancement of data transmission of data. With the sample size of 46 and tested around 11 number of times compared with the sequence analysis method. The speed with the accuracy of 92% for data transmission for wallets with merchant payment systems using cluster classification, provides significantly better results compared to sequence analysis methods. There was a statistical significance between cluster classification and sequence analysis *** improvise a data transmission for a wallet with a merchant payment system using cluster classification over sequence analysis method provides more significant accuracy of 94.4 percent than sequence analysis method by analyzing the parameters of time with data and received time.

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Energy Efficiency and Green computing in Large-scale data Centers

Energy Efficiency and Green Computing in Large-scale Data Ce...

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Evolutionary algorithms and Soft computing Techniques (EASCT), international conference on

作者： Jianyong Gao Fangjun Li Jing Wang Qiong Wang Najuan Jia State Grid Gansu Electric Power Company Lanzhou Gansu China

With the development of energy efficiency measurement methods, the target demand for energy utilization in data centers is constantly improving, and the study of green energy efficiency (EE) factors in large-scale data centers based on DEA (data envelopment analysis) is becoming increasingly important. In the construction of the entire EE factor analysis model, how to improve the green energy efficiency of the improved model and improve the efficiency of system power usage is currently a key issue that needs to be urgently solved. This article used traditional research methods on factors affecting green energy efficiency, analyzed the hierarchical composition of EE in large-scale data centers (DC), and calculated the four major energy efficiency indicators in DC. Based on the data results, the following conclusions were drawn through discussion. Through simulation experiments, six data center samples from different regions were selected, and the DEA based green energy efficiency factor analysis model was applied. Compared with traditional schemes, the green energy utilization rate has increased, with a comprehensive average improvement of 11.1%. At the same time, the power utilization efficiency has also improved, with a comprehensive average decrease of about 7.7%. This indicates that the large-scale data center green energy efficiency factor analysis model based on DEA has good results in practical applications. This study provides strong guidance and reference for the sustainable development of future data centers, and innovative solutions to address the growing digital demand and environmental challenges.

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Architecting a big data-driven software architecture for smart street lighting

Architecting a big data-driven software architecture for sma...

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IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Mubashir Ali Patrizia Scandurra Fabio Moretti Laura Blaso DIGIP University of Bergamo Italy Smart Cities and Communities (TERIN-SEN-SCC) lab. Enea Rome-Ispra Italy

Big data is one of the enabling technologies of the vision of Industry 4.0. Technological evolution is able to generate an increasing number of data. From the web, to social networks, from mobile devices to sensors, data is conveyed through the most disparate products of technology, whether physical or virtual. However, the transition from big data to smart data providing insights about information and issues that matter is not simple and obvious to achieve. The greater the amount of data and the more heterogeneous they are, the more complex their processing will be. The data, once collected, is processed by complex analytics algorithms and, to do this, considerable storage units and computing power are *** this paper, we describe our approach and experience at the Italian national agency ENEA in architecting a big data-driven software architecture for public street lighting. Such a software architecture is called ENEA PELL smart city platform (in brief, PELL SCP) and it is intended to collect, represent, control, predict, and possibly optimize the behaviour of public street lighting plants. In particular, we provide an overview of the analytics features that are being developed in collaboration with the University of Bergamo (Italy) to analyze electric energy data as collected by the PELL SCP.

关键词： Software architecture Social networking (online) Smart cities Software algorithms Lighting Big data Software

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IoT-Based Smart Wearable Devices Using Very Large Scale Integration (VLSI) Technology 1

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6th international conference on Soft computing and Signal processing, ICSCSP 2023

作者： Ashwin, M. Naidu, R. Ch. A. Ramamoorthy, Raghu Kumar, E. Saravana Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Computer Science and Engineering The Oxford College of Engineering Karnataka Bengaluru India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

People’s usage of smart wearable devices and sensors plays a crucial role in VLSI technology. The wearable devices are embedded in clothes, smartwatches, and accessories. The wear gadgets like smart rings, smartwatches, and smart spectacles are associated with human healthcare monitoring, actual period location discovery and portable online games, etc. This paper presents a survey of the recent usage of wearable devices and sensors with VLSI technology. Three key features considered to make user-friendly smart wearable devices: They are security, performance, and deployment cost. Almost all wearable devices are equipped with Bluetooth communication and screen displays like smartwatches and smartphones. The primary goal of this research is to minimize the power consumption for wearable devices. Smart wearable devices are utilized for a wide range of applications like medicine, sports, fitness, business, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wearable sensors

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Cryptographic Approach for Cloud data Security

Cryptographic Approach for Cloud Data Security

引用

Sustainable Communication Networks and Application (ICSCNA), international conference on

作者： Nagarjuna Karyemsetty NagaSai Madala Manas Panikkar Pothina Tejo Sai Nivas Anumala Vishnu Vardhan Badugu Samatha Department of CSE. Koneru Lakshmaiah Education Foundation Vaddeshwaram India

The objective is to securely store information in the cloud by dividing the encryption between multiple methods and storing it on the cloud. The aim is to maintain the confidentiality, integrity, and availability of the data. In the IT sector, many businesses are adopting cloud computing at a rapid pace, which reduces the cost of new software. Cloud computing offers the advantage of easily accessing data at a minimal cost. It is a cost-effective and convenient way to access data over the Internet. However, ensuring the integrity of cloud computing is crucial, especially since cloud storage providers may not always be trustworthy. This is particularly challenging when users store sensitive data with unreliable cloud storage providers. Therefore, securely exchanging data and maintaining data integrity remains a complex problem. This approach involves utilizing AES, DES, and RC2 algorithms to store data in a single cloud, ensuring the security and privacy of client-sensitive information.

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AI Games and algorithms: An Overview of Categories

AI Games and Algorithms: An Overview of Categories

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international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Shaidah Jusoh Hejab Al Fawareh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Sepang Selangor Malaysia School of Computing and Data Science) Xiamen University Malaysia Sepang Selangor Malaysia

AI games are one of the growing fields along with the advancement of computing technologies. Many computer games have been deployed as AI games. To the best of our knowledge, there is no clear category of algorithms used for AI games. The aim of this paper is to survey and classify AI games according to their algorithms. In this paper, AI games are classified into two categories: AI games that have been developed using traditional AI methods and AI games that have used machine learning (ML) approaches. The classifications highlight genres, techniques, and approaches used in game development. Researchers and developers in the field of AI games can use this paper as a quick reference to further explore the use of AI and ML algorithms in games.

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Analysis of Marketing data Monitoring System Based on Multi-Objective Optimization Algorithm

Analysis of Marketing Data Monitoring System Based on Multi-...

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data Analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Gao Shujing Jingu College Tianjin Normal University Tianjin China

This paper will improve the accuracy of sales forecasting from the perspective of multi-objective optimization. A two-layer multi-objective optimization model is constructed to optimize the utility of data providers and data consumers based on data quality, data attributes, correlation of attributes, consumer competition and other multidimensional factors. Then, according to the sales information, the supermarket sales decision support system based on data warehouse, on-line analysis and data mining is designed and developed. This paper introduces the construction process of data warehouse and the realization of on-line analysis and processing technology from the perspective of application. Simulation results show that the proposed algorithm achieves better performance in marketing data analysis. It enables more efficient data transactions.

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