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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification

Industry 4.0 based Machine Learning Models for Anomalous Pro...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Kumar, Sourabh Chandra, Saroj Kumar Shukla, Ram Narayan Panigrahi, Lipismita Computer Science and Engineering Op Jindal University Center of Excellence in Industry 4.0 Raigarh India Kalinga Institute of Industrial Technology Department of Computer Applications Bhubaneswar India

ISBN: (纸本)9781665492942

Automation has made tremendous changes in the industries. It has been used to automate the manual processes involved in different physical units of the industries. The purpose was to increase the production in the manufacturing. Now, computers are being used in the industries to monitor functionalities of different production units with the help of artificial intelligence and internet of things (IoT). The IoT has revolutionized the industries. It is an interconnected network system of physical units. The core purpose of it to gather and share information among different physical units. The IoT has great impact on the many areas such as business, industry, medicine, the economy, transport, industrial robots and automation systems. IoT with artificial intelligence has wide range of industrial applications. Industry 4.0 is used in the industries where different industrial units are connected over the internet and interacting to make decisions via machine-to-machine communication. It has increased the benefits of industries in terms of production and supply chain management. Manufacturing industry monitors its production units in every 10 milliseconds to capture features of the product that is being produced. The features generated in this process are huge in amount. Critical observation is performed on the generated features to categorize the product as anomalous or good. Product classification is difficult task in the labeled datasets due to human bias in labeling the final product as anomalous or good. In this work, machine learning models is being used to detect and classify faulty product produced by manufacturing industry. Both qualitative and quantitative study will be carried out to compare various machine learning models. © 2023 IEEE.

关键词： Internet of things

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Secure Electronic Health Record Storage and Retreival Using Blockchain and Encryption for Healthcare Application

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SN computer science 2023年第3期4卷 300页

作者： Saif, Sohail Mondal, Nabin Biswas, Suparna Department of Computer Applications Maulana Abul Kalam Azad University of Technology West Bengal Kolkata India Department of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal Kolkata India

With the rapid advancement of digital technologies, the electronic health record is not a concept but a reality. Electronic health records (EHR) are the patients’ health information that are stored for further reference. These recorded data in the form of electronic media are known as electronic health records and contain sensitive information about the patients. To ensure security and privacy, sensitive health records must be stored and shared through a secure communication channel ensuring confidentiality, authenticity and integrity. Blockchain provides the trust between healthcare service providers and patients i.e. healthcare service receivers. Blockchain also offers decentralization operation and transparency, which makes any application built using it more secure and also protects unauthorized access. This paper presents a blockchain-based model to solve the issue and challenges of securing the health records in an efficient and secure manner. Several popular encryption algorithms such as AES, DES, RSA, Rabbit and RC4 have been considered and implemented in this work. We have experimentally analyzed the latency to securely store and retrieve EHRs using blockchain. Experimental results show that the blockchain model with the AES-256 algorithm has the least average end-to-end delay of 635.25 ms. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain EHR Encryption Integrity Latency Privacy

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Theoretical Evaluation of Ensemble Machine Learning Techniques 5

Theoretical Evaluation of Ensemble Machine Learning Techniqu...

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

作者： Shah, Milind Kantawala, Harsh Gandhi, Kinjal Patel, Rohini Patel, Kinjal A. Kothari, Ankita Department of Computer Science and Engineering Gujarat Vadodara India Department of Computer Engineering G H Patel College of Engineering and Technology Gujarat Vallabh-Vidhyanagar India Gujarat Law Society University Faculty of Computer Applications and Information Technology Gujarat Ahmedabad India

ISBN: (纸本)9781665474672

The use of ensemble techniques is widely recognized as the most advanced approach to solving a variety of problems in machine learning. These strategies train many models and combine the results from all of those models in order to enhance the predictive performance of a single model. During the period of the last several years, the disciplines of artificial intelligence, pattern recognition, machine learning, neural networks, and data mining have all given a considerable consideration to the concept of ensemble learning. Ensemble Learning has shown both effectiveness and usefulness across a broad range of problem domains and in significant real-world applications. Ensemble learning is a technique thatinvolves the construction of many classifiers or a group of base learneis and the merging of their respective outputs in order to decrease the total variance. When compared to using only one classifier or one base learner at a time, the accuracy of the results achieved by combining numerous dassifiers or the set of base learners is greatly improved. It has been shown that the use of ensemble methods may increase the predicted accuracy of machine learning models for a range of tasks, including classification, regression, and the identification of outliers. This study will discuss about ensemble machine learning techniques and its various methods such as bagging, boosting, and stacking. finally, all the factors involved in bagging, boosting, and stacking are compared. © 2023 IEEE.

关键词： Adaptive boosting

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Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network 8

Optimized Smart Agricultural Production Systems using Enhanc...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, S. Gomathi Goyal, Himanshu Rai Lavanya Muthukumar, E. Megha, Mamtaben Kamleshbhai Parikh, Swapnil Peri College of Arts and Science Pg Department of Computer Science and Computer Applications Tamil Nadu Mannivakkam600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Viruthunagar626126 India Nehru College of Management Department of Mba Coimbatore Tamil Nadu Thirumalayampalayam641105 India Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of Cvm University Department of Computer Science Gujarat Vallabh Vidyanagar388120 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India

ISBN: (纸本)9798350376425

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). This system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced Parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. The EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). These results highlight the efficiency of the approach and its potential for further advancement in smart agriculture. © 2024 IEEE.

关键词： Emperor Penguin Optimizer (EPO) Enhanced Parallel Convolutional Neural Network (EPar-CNN) Green Anaconda Optimization (GAO) Internet of Things (IoT) Smart Agricultural Systems

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Diagnosing Phytophthora infestans infestations on Solanum tuberosum leaves using Machine Learning Classifiers

Diagnosing Phytophthora infestans infestations on Solanum tu...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Kumar, Indrajeet Kumar, Sanjeev Kumar, Padmanavan Harshith, John Dutta, Anurag Government College of Engineering and Textile Technology Department of Information Technology Serampore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India G L Bajaj Institute of Technology and Management Department of Master of Computer Applications Greater Noida India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Vellore Institute of Technology Department of Computer Science and Engineering Vellore Campus Vellore India Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore India

ISBN: (纸本)9798350323917

In reaction to recent technology breakthroughs, modern computational fields such as the IoT, Data science, Machine Learning, AI, and several more have arisen. Humans benefit greatly from these fields. In this study, we would employ parts of machine learning to forecast plant illnesses. Particularly amongst the potato leaves, severe Late Blight Disease was discovered. The potato plant, also known as Solanum tuberosum, is a significant crop that is planted all over the world and yields copious amounts of nutritious tubers. The potato is a frequent staple food and has several health benefits. When ingested in moderation, the fluid extracted from tubers can cure stomach ulcers and reduce inflammation and acidity. Both Early and Late Blight are dangerous and widespread potato diseases. We would concentrate on Late Blight in this effort. A fatal fungus that harms potatoes is called Late Blight. It is accessible everywhere in the world. It can be found in areas of the world where potatoes are grown. Winter is the primary growing season for potatoes in India. It is followed by sweltering summer months in the lowlands. Heat and dryness kill the fungus that lives in the soil. The fungus Phytophthora Infestans, the most prevalent agricultural disease that causes the Late blight of potatoes, can quickly result in crop failure if appropriate control measures are not adopted. Potato crop declines in epidemic years might be as high as 80%. We acquired a collection of roughly one thousand samples of Late Blight-affected leaves of potatoes for this investigation. With the help of conventional classifier algorithms, we have developed a machine-learning classification paradigm that may be able to forecast the onset of Late Blight disease. Medical science has developed significantly. Plant pathology could be able to prevent the threat from happening if we could perhaps predict the illness in its early stages. © 2023 IEEE.

关键词： Diseases

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Supply Chain Strategies Supported by Blockchain Technology: Open Problems and Challenges

Supply Chain Strategies Supported by Blockchain Technology: ...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Rana, Sumit Kumar Rana, Arun Kumar Naz, Sardar M N Islam Singh, Ashutosh Kumar Bhushan, Bharat Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Panipat India Galgotia College of Engineering Knowledge Park I Department of Computer Science Engineering Uttar Pradesh Greater Noida201310 India ISILC Victoria University Melbourne Australia Kurukshetra University NIT Department of Computer Applications Kurukshetra India Sharda Universi Knowledge Park III Department of Computer Science Engineering Greater Noida201310 India

ISBN: (纸本)9798350396485

The most prevalent issue in the supply chain ecosystem is introducing transparency to the system. Another issue is fostering trust among various stakeholders to support successful process execution. Depending on the domain and the stakeholders, the supply chain process may change. Thus, solving the issue is challenging. Blockchain technology, sometimes referred to as distributed ledger technology, is beneficial in many ways in a variety of industries, including finance and healthcare. Blockchain based model can resolve the issues of traditional supply chain ecosystem. It creates a decentralized and transparent system where every decision is taken with the help of consensus of the associated stakeholders. This supports an immutable ledger so unauthorized modification is not supported. In this article, we address the issues that the supply chain ecosystem faces as well as the benefits of blockchain technology in resolving these issues. As well as some of the remaining challenges and opportunities, other aspects of blockchain technology are explored that will enable it flourish in future supply chains. © 2023 IEEE.

关键词： Supply chains

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Analytical Evaluation of Web Performance Testing Tools: Apache JMeter and SoapUI 12

Analytical Evaluation of Web Performance Testing Tools: Apac...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tiwari, Vatsya Upadhyay, Sachin Goswami, Jayati Krishna Agrawal, Sanjiv Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India GLA Univ. Deparmtnent of Computer Engineering & Application Mathura India G L Bajaj Group of Institutions Computer Sciecnce & Engineering Mathura India

ISBN: (纸本)9781665462617

Computing on the Web is based on distributed system applications that use the client-server architecture. Since web-based applications have been beta-tested, the quality criteria cannot be fixed. Whenever a website is launched, the question is always raised as to how many HTTP requests of users can the website handle at once. As part of this paper, various tools for performance testing will be used, including Apache SoapUI, JMeter, etc., and selection tools for load testing will be discussed. © 2023 IEEE.

关键词： Load testing

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Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehensive Study using XGBoost Classifier and Feature Selection Techniques

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Procedia computer science 2024年 235卷 3458-3467页

作者： Rekha R Nair Tina Babu Kishore S Vishnu Vilashini S Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru Karnataka India

Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue.

关键词： XGBoost Classifier SelectKBest Singular Value Decomposition Principal Component Analysis hepatocellular carcinoma

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Statistical validations on energy efficient routing protocols to improve the performance of Wireless Sensor Network 1

Statistical validations on energy efficient routing protocol...

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1st International Conference on Machine Learning, computer Systems and Security, MLCSS 2022

作者： Chithaluru, Premkumar Jena, Lambodar Singh, Debabrata Nayak, Soumen Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India University Department of Computer Applications Odisha Bhubaneswar India University Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9781665454933

In a Wireless Sensor Network (WSN), there is a need for research in design a well-suited routing protocol used for remote applications, and it may resolve the issues related to energy, packet delay, throughput. However, the primary constraint of sensor networks has limited energy sources. An approach is required for enhancing the energy usage that leads to improving the network lifetime. An enhanced clustering protocol designed, and it has shown the energy -efficient in terms of reducing energy usage and extending the network lifetime. The proposed Adaptive Ranking based Energy Efficient Opportunistic Routing (AREOR) [8] protocol consists of two phases, one is an adaptive ranking mechanism to grade the Forwarder Nodes (FNs) and Cluster Head (CH), and the other one is the opportunistic forwarding scheme for optimal energy usage of each Sensor Node (SN). In the adaptive ranking technique, parameters highest residual energy and the minimum distance from the source node (Ns) are considered the electing CH in every transmission. The proposed protocol improves the network lifetime and leads to improvement in Message Success Rate (MSR), End-to-End delay (E2 delay), Packet Delivery Ratio (PDR), and Energy Consumption (EC). It was observed that the AREOR protocol showed the better statistical validation (rejects the null hypothesis) compared to traditional routing protocols with the total number of samples 30 and the Confidence Interval (CI) is 0.85 considered for simulation. © 2022 IEEE.

关键词： Energy utilization

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