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Implementation of Ensemble Predictive Models for Parkinson’s Disease Detection 5th

Implementation of Ensemble Predictive Models for Parkinson’...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Ruth, J. Anitha Mahesh, Vijayalakshmi G. V. Uma, R. Department of Computer Science and Applications SRM Institute of Science and Technology Vadapalani Campus Tamil Nadu Chennai India Department of Electronics and Communication BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819761029

A framework based on ensemble classifier models is presented in this study, which may be used to predict the presence of disease and to increase the accuracy of the prediction. Machine learning's ensemble technique pools several different classification algorithms and an extensive number of classifier models to get more precise predictions. In this present study SVM and DT are employed as base estimators, which takes use of well-known ensemble techniques, such as bagging and boosting, to construct ensemble classifiers. The proposed technique is tested on a Parkinson's database taken from machine learning repository and is found to be effective. Classification accuracy (CA), precision, recall, area under curve (AUC), Cohen kappa score (CKS), Mathews correlation coefficient (MCC), and F1-measure obtained from receiver operating characteristics (ROC), as well as the MCC and F1-measure indicate that the ensemble classifier model has enhanced prediction performance with the least possible errors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neurodegenerative diseases

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Innovations in Agri-Tech: A Review of Artificial Intelligence applications and Challenges in Modern Agriculture 2

Innovations in Agri-Tech: A Review of Artificial Intelligenc...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Rupanshi Bhardwaj, Isha Sharma, Ashish Kumar Sanghi, Akash Agarwal, Gaurav Invertis University Department of Computer Science and Engineering Bareilly India Kothiwal Institute of Technology and Professional Studeis Department of Applied Science and Humanities Moradabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties. © 2024 IEEE.

关键词： Smart agriculture

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An Adaptive-based Predicted Nodes Behavior Algorithm in Flying Ad Hoc Networks 5

An Adaptive-based Predicted Nodes Behavior Algorithm in Flyi...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Aruna, Orchu Sharma, Amit Vasireddy Venkatadri Institute of Technology Computer Science and Engineering AP India Lovely Professional University School of Computer Applications Andhra Pradesh India

ISBN: (纸本)9781665474672

This paper discusses implementing unmanned aerial vehicles (UAVs) for various military & civil parts with the creation of flying ad-hoc networks (FANETs), as recent applications include remote aerial nodes. Communication among UAVs in FANETs must be flexible, dependable, and delay-bounded so that network can continue to provide the needed level of service while also accommodating the ever-increasing number of UAVs. Data routing between UAVs in a FANET is much more complex than in a MANET due to the more significant mobility requirements of the former. Modifications to the network's topology need real-time adjustments to the routing tables. FANET provides a reliable connection between UAVs by ignoring metric computations of most current routing algorithms. Due to the extreme mobility of nodes in FANET, route reliability is also a significant issue. Based on these findings, this study presents PF-WGTR, a novel routing method. A consistent routine route is determined using a weighted algorithm that predicts the future values of specific parameters at each node in a FANH F. The suggested routing method uses each node's predicted future value to determine the node's weight within the network. This enables the communication nodes to set up a dedicated and stable route over a lengthy period. This dynamic routing system optimizes energy usage by looking ahead and making predictions. It reduces the amount of data lost in transit while maintaining the same or greater throughput than current routing protocols. The proposed protocol PF-WGTR could achieve laetter results than others hised on the valuation of simulation results. © 2023 IEEE.

关键词： Military vehicles

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Network Media Platform to Build a Sustainable Development of Ecological Environment Using Blockchain Technology 3

Network Media Platform to Build a Sustainable Development of...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sundar, G. Patchaiammal, P. Swetha, V.P. Rengarajan, Manikandan Satheesh, Nimmati Sasikala, P. Sindhi College Department of Computer Applications Chennai600077 India Panimalar Engineering College Department of MBA Chennai India RandD Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Avadi Chennai6000627 India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Department of Computer Science Bangalore India

ISBN: (纸本)9798350396157

Social technology has grown rapidly and effectively as a result of new information technology. The importance of the big data approach, which was among the most prominent current occurrences, has improved as a result of the amount of information generated from social media. For the installation of renewable energies, the Energy Network seems to have become a central feature. Because there are so many different sustainable energy sources and how they interact with the grid, the system is extremely complex and presents a number of intrinsic challenges, such as the coordination and monitoring of distributed sustainable energy generation, which can be extremely difficult to handle. This study presents a blockchain-based approach for managing digital information in a network as a solution to these problems. Blockchain is a distributed ledger that implements agreement, inevitability, and transparency for online data using encryption schemes, hash chains, and consensus mechanisms. The suggested approach can employ blockchain's features to achieve efficient network content creation management, copyright management, system administration, and user instructional techniques. Our suggested method can consistently provide the network media platform with crucial assistance, enabling it to pursue long-term, ecologically sustainable expansion. © 2024 IEEE.

关键词： Smart contract

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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. The aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. Then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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Prediction of Algae Growth: A Machine Learning Perspective 2023

Prediction of Algae Growth: A Machine Learning Perspective

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15th International Conference on Contemporary Computing, IC3 2023

作者： Tiwary, Sanjeeb Darshana, Subhashree Mohanty, Debabrata Dash, Adyasha Rupsa, Potnuru Barik, Rabindra K School of Computer Science Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798400700224

Algal blooms pose a significant threat to aquatic ecosystems and human health. To address this issue, this paper proposes a machine learning-based approach for predicting harmful algal blooms (HABs) by analyzing environmental features. Algae, as primary organic matter and oxygen producers, play a vital role in the biosphere. However, the exponential increase in algal growth worldwide poses significant challenges to economic development and long-term sustainability. The paper employs three popular machine learning algorithms: Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) to predict algal blooms. The research utilizes real-time data from two locations: the Sassafras River in the United States Chesapeake Bay and Lake Okeechobee in Florida, USA. These locations have experienced frequent HABs due to factors like chemical runoff and nutrient-rich conditions. By analyzing the collected data, the paper identifies and selects the most important features to optimize the prediction models' accuracy. Preliminary results demonstrate promising accuracy in predicting algal growth and identifying key characteristics associated with HABs. These findings contribute to a better understanding of algal blooms and pave the way for effective mitigation strategies to combat this global environmental challenge. © 2023 ACM.

关键词： Algae

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A Comprehensive analysis of 5G Communication and Its Health Care Management Models 2

A Comprehensive analysis of 5G Communication and Its Health ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar Sagar, Lalit Goel, Dipti Gupta, Sonali Saraswat, Manish Sharma, Ashish Srm Institute of Science and Technology Delhi Ncr Campus Department of Computer Applications UP Modinagar India Raj Kumar Goel Institute of Technology Department of Information Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Gla University UP Mathura India Gla University Department of Computer Engineering & Applications UP Mathura India

ISBN: (纸本)9798350371055

This paper provides an in-intensity examination of 5G conversation and its health care management models. 5G era, the imminent 5th technology of cellular cell communications, is expected to revolutionize the cellular enterprise with its build-up of excessive velocity, low latency, and big capability. however, it also increases new demanding situations for healthcaredelivery, in addition to data protection, privateness and tool management. This paper will are seeking for to deal with those troubles by using exploring numerous 5G fitness care management fashions. extra mainly, this paper will discover the demanding situations of 5G in healthcare, how the current healthcare infrastructure can facilitate 5G's use, and the principle elements of numerous 5G healthcare management fashions. finally, this paper will discuss the destiny implications of 5G in healthcare, and what groups want to do in order to make certain the most green and cozy healthcare shipping. by means of delving into this rely, this paper will provide a complete view of 5G conversation and its healthcare control fashions, main to progressed healthcare delivery within the close to future. This paper presents a complete analysis of fifth-technology (5G) communique applied to healthcare control fashions. To make sure functionality and efficacy., 5G networks and their related services need to adhere to a strict set of requirements and requirements, which can be evaluated in regard to latency, community useful resource requirements, and safety protocols. several important additives and capabilities of 5G-enabled healthcare models are mentioned, which include far off patient monitoring offerings, personalized healthcare services, and pervasive healthcare get right of entry to. additionally, this paper discusses the measures for successful implementation of 5G technology into healthcare, in conjunction with some potential dangers and ethical issues that have to be taken under consideration. in the end, the pap

关键词： cellular demanding models revolutionize

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Comparative Analysis of Techniques for Efficient Resource Utilization in Cloud Environments Through VM Placement Optimization

Comparative Analysis of Techniques for Efficient Resource Ut...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Natarajan, P. Panjatcharam, V.G. Rajkumaran, T. Erode Sengunthar Engineering College Dept of Mca Erode India Vet Institute of Arts and Science Dept of Computer Science Erode India Vet Institute of Arts and Science Dept of Computer Science '&' Applications Erode India

ISBN: (纸本)9798350300857

This study focuses on evaluating and comparing different VM placement techniques in a simulated cloud environment. The performance metrics, including resource utilization, load balancing, number of servers used, average response time, fault tolerance, and storage capacity, are analyzed for each technique. The results indicate that the Supervised technique achieves the highest resource utilization, load balancing, and storage capacity, along with the lowest average response time. The Bin Packing technique demonstrates efficient server utilization and medium fault tolerance. The Load-Aware and Genetic techniques exhibit a trade-off between resource utilization, load balancing, and fault tolerance. These findings provide valuable insights into selecting appropriate VM placement techniques based on specific requirements. © 2023 IEEE.

关键词： Virtual machine

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sharma, Divya Sharma, Shilpa Patil, Harshal Bhatnagar, Vaibhav Manipal University Jaipur Department of Computer Applications Jaipur India Symbiosis Institute of Technology Symbiosis International Computer Science and Engineering Pune India

ISBN: (纸本)9798350335774

Image dehazing and image defogging work towards the same goal, which is to reconstruct hazy or foggy images by eliminating noise. Among the many dehazing and defogging algorithms available, Dark Channel Prior (DCP) is among the most effective algorithm. DCP employs the dark pixels from the noisy image to perform image reconstruction using the standard four-step process. In this paper, we implement DCP single image dehazing for license plate images. Specifically, the transmission map refinement was implemented using the guided filter. The implemented model performs the DCP implementation along with its five important parameter evaluations and analyzes the performance. The paper concludes with a comparative analysis for five parameters that are patch size, epsilon value, window radius, value of and finally pixel values. © 2023 IEEE.

关键词： Image reconstruction

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Soil Analysis Using Deep Learning for Precision Agriculture 1

Soil Analysis Using Deep Learning for Precision Agriculture

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Fotabong, Thierry Forsack Muhammad, Abdullahi Keisham, Murphy Mehrotra, Tushar Singh, Rajneesh Kumar Singh, S. Pratap Agrawal, Arun Prakash Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Symbiosis Institute of Technology Nagpur Campus Deemed University Pune India

ISBN: (纸本)9798350365016

This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study addresses the limitations of traditional soil classification methods, which are often labor-intensive and reliant on expert judgment. The proposed approach involves collecting soil image data, preprocessing it using techniques such as Gabor filtering, and classifying the images with a CNN model. The methodology includes data acquisition, feature extraction, and model training, aiming to accurately predict soil parameters using diverse input data sources like sensor readings and satellite imagery. The potential benefits of this approach include real-time monitoring, optimal fertilizer application, and enhanced crop management, which can contribute to sustainable agricultural practices and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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