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Big Data Analytics for Quality Control in The Pharmaceutical Industry 10

Big Data Analytics for Quality Control in The Pharmaceutical...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Malathi, P. Jansi, S. Prabhakaran, M.V. Senbagam, K. Sujatha, M.P. Sadaieswaran, R. Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Acharya college of Engineering and Technology Department of Computer science and Engineering Achariyapuram Puducherry India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Agni College of Technology Department of Computer Science and Engineering Chennai Thalambur India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India

ISBN: (纸本)9798350384369

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns. © 2024 IEEE.

关键词： Data quality

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An IOT-Based Soil Moisture Management System for Precision Agriculture: Real-Time Monitoring and Automated Irrigation Control 4

An IOT-Based Soil Moisture Management System for Precision A...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Laha, Suprava Ranjan Pattanayak, Binod Kumar Pattnaik, Saumendra Mishra, Debashree Kumar Nayak, Debasish Swapnesh Dash, Bibhuti Bhusan Iter Siksha O Anusandhan University Department of Computer Science and Engineering Odisha Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350300888

Managing soil moisture is crucial for optimizing crop growth and yield in agriculture. With the advancements in Internet of Things (IoT) technology, innovative soil moisture monitoring and control solutions have emerged. This paper presents a pioneering IoT -based soil moisture management system tailored for precision agriculture within Siksha 'O' Anusandhan (SOA) University. The system utilizes sensor nodes, wireless communication, and cloud computing to enable real-time monitoring and automated irrigation control, addressing the limitations of conventional manual approaches. Providing continuous, remote, and precise measurements of soil moisture levels revolutionizes how soil moisture is managed within the university's agricultural practices. Low-cost sensor nodes are strategically placed throughout the fields to collect data, which is wirelessly transmitted to a central hub for analysis and decision-making. A cloud-based platform processes the data, generating actionable insights and triggering automated irrigation when necessary. Extensive testing and validation demonstrate the system's reliability and ability to improve water use efficiency, surpassing traditional methods. Furthermore, the IoT architecture of the system offers scalability and flexibility, making it adaptable to different crop types and field conditions present within SOA University. This innovative research contributes to the field of precision agriculture by providing an efficient and tailored solution for soil moisture management within the university's agricultural practices. This IoT -based system enhances crop productivity by enabling real-time monitoring and automated irrigation control, promoting sustainable water resource utilization. Its implementation within SOA University fosters the integration of cutting-edge technology into agricultural practices, facilitating optimized crop growth and contributing to the university's commitment to environmental sustainability and resource managem

关键词： Soil moisture

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Predicting JVM Parameters for Performance Tuning Using Different Regression Algorithms 4

Predicting JVM Parameters for Performance Tuning Using Diffe...

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Vijayakumar, G. Bharathi, R.K. JSS Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9781665456357

The Java Virtual Machine provides runtime environment to java program. It is responsible for loading class files, checking the code, interpreting them, and executing them. Whereas application is run as a thread inside the JVM. The garbage collector thread is launched by the JVM (when possible or necessary) without the user's intervention in order to free up utilized, unreferenced memory. In order to optimize memory and garbage collection settings and to troubleshoot memory-related problems, one has to analyze Garbage Collection logs. An application's overall performance while it is running is significantly impacted by garbage collection, an automated memory management system is vital for Java Virtual Machine (JVM). The Java Development Kit offers a number of garbage collectors from which the developer may choose while setting up the environment. There are several different kinds of garbage collectors, including Serial GC, Parallel GC, Z GC, and Garbage First GC. We investigate the operation of the GC in a defined environment, with the focus on serial and parallel GC. Throughput and latency are the main performance indicators used in the study. The findings of the study demonstrate that different applications need different garbage collector configurations. It is possible to improve latency levels. This study also demonstrates how GC parameters, namely adjusting the heap size and number of threads, will impact the performance and latency of an application, how different regression algorithms better suited for different use cases. This study will also investigate the effects of a set of optional GC settings in addition to the default setups on application performance, which is another part of its contribution. Finally, we will be able to anticipate the appropriate heap size needed for the applications smooth functioning, the required application duration, and the quantity of GC and Full GC events during the application's runtime. In summary we predict the parameters u

关键词： Parameter estimation

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Resource Management on Cloud Computing Using Machine Learning 6

Resource Management on Cloud Computing Using Machine Learnin...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Srivastava, Devesh Kumar Gupta, Sumit Kumar Tiwari, Pradeep Kumar Kaur, Manjiit Manipal University Jaipur Department of Information Technology Jaipur India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India S R University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798331516796

Efficient resource management within cloud computing stands as a multifaceted challenge, involving the dynamic allocation and scaling of resources to suit the ever-shifting demands of applications and workloads. Conventional resource management systems hinge on rigid, static policies, often resulting in inefficiencies, leading either to resource overprovisioning or under-provisioning. Such scenarios tend to escalate costs for both cloud providers and customers, subsequently leading to service level agreement (SLA) breaches. Consequently, the pursuit for optimal solutions in cloud computing resource management has gained significant attention among researchers and developers. Specifically, machine learning techniques have emerged as a promising avenue due to their extensive applicability in this domain. This paper presents a comprehensive survey that explores the utilization of machine learning techniques for resource management systems within the realm of cloud computing. The first approach employs the versatile K-means clustering algorithm, introducing a resource management scheme that organizes virtual machines (VMs) into clusters based on their resource requirements. Each cluster is led by a representative VM, and the efficacy of this scheme is evaluated through cloud simulation. The results demonstrate a significant improvement in resource utilization and a reduction in energy consumption. Beyond these benefits, K-means clustering exhibits potential in identifying workload clusters, anomaly detection, and optimizing resource allocation. This approach showcases the adaptability and efficiency of K-means clustering in enhancing overall cloud resource management. The second approach introduces a resource management system (RMS) in the cloud environment, leveraging a decision tree (DT) algorithm. The DT model is trained on historical resource usage data, establishing relationships between workload characteristics and resource requirements. This enables the predictio

关键词： K-means clustering

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Improve Accuracy in Advertisement Viewability Prediction using PLC and Logistic Regression 1

Improve Accuracy in Advertisement Viewability Prediction usi...

引用

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Gunasundari, R. Balakumar, C. Karpagam Academy of Higher Education Faculty of Arts Commerce Science and Management Department of Computer Applications Tamilnadu Coimbatore India

ISBN: (纸本)9798350359756

Advertising serves as a crucial driver for promoting products and building their brand presence. In contemporary times, online advertising has gained prominence, becoming a burgeoning industry for both product manufacturers and online publishers. Anticipating and catering to user interests and preferences is of paramount importance when determining the types of advertisements to display on a web page to maximize the number of views and clicks. To address this challenge, our research introduces a hybrid model that combines the probabilistic latent class model and a logistic regression classifier. This model operates in two distinct stages. The Probabilistic Latent Class (PLC) model is employed for the initial stage of advertisement viewability prediction. In existing studies, web page attributes, particularly scroll depth and browser behavior, are utilized for this purpose. However, our proposed approach incorporates additional features such as dwell time, scroll depth level, gender, ad type, and viewed status to enhance viewability prediction using the PLC model. In the second stage, we use a Logistic Regression classifier to categorize the gathered ad view data and determine viewability accuracy. The predicted dataset is retrieved from the database and employed for the classification process via Logistic Regression. © 2023 IEEE.

关键词： Forecasting

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Information system for flood monitoring based on IoT and AI 2

Information system for flood monitoring based on IoT and AI

引用

2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Wilson, Allan J Pon Bharathi, A. Anoop, M. Angelin Jeba Malar, J. Amrita College of Engineering and Technology Department of Electronics and Communication Engineering Nagercoil India Alpha Arts and Science College Department of Computer Applications Chennai India A.M.Jain College Department of Computer Science Chennai India

ISBN: (纸本)9798350348002

One of the most significant areas where realistic solutions have been put into place to minimise harm is flood management. In this research project, we are creating an IoTenabled adaptive AI technique for effective fluid management. Floods in Kerala, India, have recently seriously damaged the environment, infrastructure, and population. Various IoT devices gathered information on temperature, precipitation rates, and other variables. This study uses a real-time flood visualisation technology to provide an information system for flood monitoring. A system is constructed by first creating two systems: a client system comprised of hardware for detecting floods and a server system comprised of software for monitoring floods and distributing the collected data. All of the information on the flood in Kerala is compiled and split in halves, with the first set serving as training data and the second as test data. The suggested hybrid model is trained using six different machine learning models, and it achieves a maximum accuracy of 99.64%. We were able to prevent losses because to the system's seamless design. Research reveals that the client of the flood detection system may deliver real-time data on flood levels, weather conditions, and temperatures. Additionally, data from client systems can be collected by flood monitoring information systems and stored in MySQL. © 2023 IEEE.

关键词： Visualization

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FinTech Edge: Utility Computing & Artificial Intelligence Technologies for Smart Financial Acquisition & Blockchain in the Financial Industries 7

FinTech Edge: Utility Computing & Artificial Intelligence Te...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, Sandeep Sarkar, Tiyas Mangla, Monika Rakhra, Manik Singh, Amanpreet Jairath, Kapil Lyallpur Khalsa College Department of Computer Science Punjab Jalandhar India Lovely Professional University School of Computer Science & Engineering Punjab Phagwara India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai India Lovely Professional University School of Computer Science & Applications Punjab Phagwara India Trinity College Department Of Sciences Punjab Jalandhar India

ISBN: (纸本)9798350350067

The study ventures into the changing environment of blockchain, artificial intelligence (AI), and cloud computing as key components of financial technology (FinTech). This research examines how these technologies are transforming the financial system, with a focus on intelligent finance investment including blockchain's disruptive potential. It begins with a brief description of cloud computing, explaining how it promotes extensibility, formability & cost-efficiency inside financial Institutions. The focus then shifts to AI applications, including the impact of robot advisers, trading using algorithms as well as statistical research on the reinvention of investing processes. The tale probes further into complex architecture of blockchain technology, giving light on its ability to increase the security and openness of the financial system. It reveals linkages that open the path for novel methods and significant developments, emphasizing the convergence of cloud, AI & blockchain. As we explore these FinTech territories, the paper acknowledges the inherent obstacles and dangers while also providing thoughts on legislative and ethical implications. It examines emerging trends, predicting the long-term influence of novel innovations on the financial environment. © 2024 IEEE.

关键词： Decentralized finance

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Ensemble Feature Reduction Technique for Flower Species Identification

Ensemble Feature Reduction Technique for Flower Species Iden...

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2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Sethi, Shilpa Kathuria, Mamta J. C. Bose University of Science & Technology Ymca Department of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350380880

Flowers are the foremost part of our biodiversity and a major source of food and medicines. Flower species identification is popular in the anthology field, due to the numerous health benefits. The identification of flowers by conventional methods is a tedious task. The availability and ubiquity of digital technologies and advancements in pattern recognition techniques, let the idea of automated flower species identification a reality. Current works had many limitations such as high inference time, high dimensionality, low accuracy and increased memory consumption. This paper provides an optimized feature selection technique to accurately identify flower species using machine learning techniques in less time. The objective of work includes balancing of the ecosystem, medicinal purposes, the agricultural industry, environmentalists, educators etc. It is observed that by reducing 50% of features, a significant gain of 44.2% in inference time without loss of accuracy is achieved. © 2023 IEEE.

关键词： Biodiversity

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HSO-Based K-Means Segmentation for Identification of Disease and Deficiency in Paddy Plant

HSO-Based K-Means Segmentation for Identification of Disease...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Sivagami, S. Mulakaluri, Sailaja Saranya, C. Ashtalakshmi, K. Kalpana, R. Manoshankari, M. Department of Computer Science and Applications St. Francis de Sales College Electronic City P.O Bengaluru560 100 India

ISBN: (纸本)9789819762217

In agriculture, image processing is frequently used to identify issues with fruit grading, weed identification, disease diagnosis, and other related tasks. In order to detect nutritional deficiencies and paddy plant disease, this study employs image processing. Image has to classified as diseased or nutrient deficient after performing segmentation of image and selection of important features from the image. There are many different image segmentation algorithms available. K-means is an incredibly easy algorithm to understand, apply, and provide accurate results with. Despite its accuracy and simplicity, its drawbacks include the requirement to estimate the value of K and choose K’s initial centroids at random from the provided data values. A unique image segmentation technique was developed to solve this problem. It is based on the identification of inadequacies using K-means and harmonic search optimization (HSO). The K-means algorithm’s performance is significantly impacted by the randomly chosen values for K and the centroid values of the clusters. The standard K-means method takes longer to produce accurate results because it needs random initialization. A K-means method based on HSO is suggested to expedite the initialization procedure. The aim function of the suggested approaches is the Otsu technique with an initial step created from the HSO. The HSO method is used to locate the cluster canters, which are then used to begin the K-means algorithm. Lastly, when segmentation results are compared to those obtained using the fuzzy C-means and classic K-means segmentation algorithms, the recommended HSO-based K-means technique performs better than the rivals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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A Prediction of Flight Fare Using K-Nearest Neighbors 2

A Prediction of Flight Fare Using K-Nearest Neighbors

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Prasath, S. Naveen Kumar M, Sathish Eliyas, Sherin School of Computer Science Hindustan Institute of Technology &science Department of Computer Application Chennai603103 India Hindustan Institute of Technology and Science Department of Computer Applications Chennai603103 India

ISBN: (纸本)9781665437899

Someone who buys plane tickets on a regular basis can be able to predict the optimum moment to buy an airline ticket in order to afford the greatest price. For revenue management, many airlines modify ticket pricing. when the demand for the ticket is high, the airline may hike the pricing. To calculate the minimal airfare, data for a given air route was gathered over a period of time, including parameters such as departure time, arrival time, and airways. To use Machine Learning (ML) models, features are retrieved from the obtained data. The cost of a plane ticket is determined by several distinct strands. The main purpose of the research is to find out the factors that impacts drive. airfare price fluctuations and how they connect to price changes. Then, based on this data, create a mechanism to assist purchasers in deciding whether or not to purchase a ticket. The KNN technique is used in this paper to estimate prices at a given time using machine learning regression approaches. © 2022 IEEE.

关键词： Air transportation

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