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To understand the relationship between Machine learning and Artificial intelligence in large and diversified business organisations

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Materials Today: Proceedings 2022年 56卷 2082-2086页

作者： Sandeep, S.R. Ahamad, Shahanawaj Saxena, Divya Srivastava, Kingshuk Jaiswal, Sushma Bora, Ashim Department of Electrical and Electronics Engineering SJB Institute of Technology Karnataka Bengaluru India Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India College of Computer Science and Engineering University of Hail Saudi Arabia Master of Science in Computer Science University of Texas at Arlington ArlingtonTX76019 United States Department of Informatics School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India Koni CG Bilaspur India Department of Mathematics. Diphu Government College Assam Diphu India

In the modern era, Machine learning and Artificial intelligence remain the most remarkable IT application, a technology that has seen unprecedented progress in recent decades. AI and machine learning are all closely linked with using a computer to simulate intelligent behaviour with little human interaction. It has been noticed that many large organisations are implementing technologies such as ML and AI to improve their performance and productivity in the business. Companies can create better, more customised and engaging campaigns as AI becomes increasingly adept at collecting massive data faster than ever before. As AI and ML technologies become more widely used, being a technology-powered company will undoubtedly be a must for survival. AI and ML are guiding on everything from production to delivering products and services to customers. Machine translation, chatbots, and self-learning algorithms are examples of AI technology that may help people better comprehend their surroundings and respond appropriately. Organisations have been embracing artificial intelligence technology advancements to create and maximising their strategic and competitive advantages. Hence, businesses rely heavily on AI to enhance their performance and develop new services to boost productivity and generate new offerings. In addition, they are fundamentally reshaping companies' business and operation processes to better meet customers’ requirements and expectations, resulting in increased efficiency. Machine learning and artificial intelligence are broad concepts that will be examined further in this article. The report aims to understand the relationship between Machine Learning and Artificial Intelligence in large and diversified business organisations. The researcher used a study methodology oriented toward a more inclusive and comprehensive approach to better account for the intangible aids of ML and AI within organisations. © 2021

关键词： Efficiency

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Comparision of Principal Component Analysis, K-means, and Generative Adversarial Network for Compression and reconstruction of medical image

Comparision of Principal Component Analysis, K-means, and Ge...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Anusha Prashanth Shetty Deepthi L Raksha Puthran Surendra Shetty Mohammad Anzil Parveez Shravan Kumar Department of Master of Computer Applications NMAM Insitute of Technology Nitte (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering (AI&ML) Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Medical images are essential in healthcare, but their large file sizes present challenges for storage and transmission. This study proposes a framework for compressing color medical images while preserving key visual information. We apply three compression models: Principal Component Analysis (PCA), K-Means clustering, and Generative Adversarial Networks (GANs), and evaluate their performance based on Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). K-Means with 100 clusters outperforms the other models, achieving the highest compression (CR of 1.20), with excellent image quality (PSNR of 66.77 dB, SSIM of 1.0000). PCA shows moderate performance with a CR of 2.45, while GAN provides the least effective compression and image quality. These results demonstrate that K-Means offers the best trade-off between compression and image fidelity.

关键词： Measurement Image quality Analytical models Image coding PSNR Medical services Generative adversarial networks Image reconstruction Principal component analysis Biomedical imaging

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OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

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Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management (IC-RVITM), International Conference on th

作者： Binju Saju V Asha Neethu Tressa R A Suhas Shah Yash Jagesh V Sandhya Department of Computer Science - AI Engineering Adi Shankara Institute of Engineering & Technology Kalady Kerala Master of Computer Applications New Horizon College of Engineering Bengaluru India

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%.

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Smart Aeroponic Farms with IoT-Enabled Efficient Automation and Monitoring 2

Smart Aeroponic Farms with IoT-Enabled Efficient Automation ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Jeyabharath, R. Tamilvani, P. Karthikeyan, G. Vijayakumar, P. Rohini, J. Mohammadha Hussaini, M. Kavin Kumar, K. K.S.R. College of Engineering Department of Electrical and Electronics Engineering Tiruchengode India M.P.Nachimuthu M.Jaganathan Engineering College Department OfElectrical and Electronics Engineering Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Kongu Engineering College Master of Computer Applications Erode Perundurai India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India Government College of Engineering Department of Electrical and Electronics Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The Internet of Things (IoT) has enabled the adoption of automated aeroponics equipment, which has transformed our understanding of horticulture. Aeroponics is a method of vegetative production that does not use soil. Plant cultivation takes place in an airborne or vapour environment. The use of Internet of Things (IoT)-enabled aeroponics devices allows farms to autonomously monitor and manage variables such as temperature, lighting, humidity, and fertiliser concentrations. This allows for real-time adjustments to be made to optimize plant growth and yield. The IoT-enabled automated aeroponics system utilizes sensors and actuators to collect data on environmental conditions and automate the delivery of nutrients and water to the plants. Additionally, the system can be programmed to send alerts and notifications to farmers in case of any irregularities or issues that require attention. This level of automation not only increases efficiency and productivity but also reduces the need for manual labor and minimizes resource wastage. The IoT-enabled automated aeroponics systems have the potential to revolutionize farming by providing a more sustainable and efficient method of crop production. The computer system uses environmental elements to evaluate the results of aeroponics studies. The decision was made to use an automated aeroponics methodology to develop maize plants. According to the findings of this study, applying this method could potentially reduce labour expenses while increasing the value of the product. © 2024 IEEE.

关键词： Internet of things

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Enhancing Operational Efficiency through the Integration of CI/CD and DevOps in Software Deployment

Enhancing Operational Efficiency through the Integration of ...

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Partha Sarathi Chatterjee Harish Kumar Mittal Master of Computer Science Liverpool John Moores University (LJMU UK) Upgrad Education India BM Institute of Engineering and Technology Sonepat India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

In the rapidly evolving digital era, the complexity and dynamic nature of software applications have significantly increased, driven by ever-changing consumer and business requirements. This shift poses a challenge to traditional software development and deployment methodologies, which often struggle to keep pace with these rapid changes. This paper explores the transition from conventional methods to agile methodologies, emphasizing their critical role in maintaining application stability and facilitating seamless updates with minimal impact on end-users. Central to this study is the examination of automated deployment models, particularly Continuous Integration/Continuous Deployment (CI/CD), and their transformative impact on the software deployment process. The research delves into the intricacies of the DevOps lifecycle, highlighting the importance of various environments such as Development (Dev), Testing (Test), and Production (Prod). These environments are crucial in ensuring that any updated version of an application is rigorously tested and free of bugs before its deployment in a production setting. Through a comprehensive case study conducted in an AWS lab environment, this paper demonstrates the effectiveness of automated deployment models in overcoming the limitations inherent in manual deployment processes. The findings reveal significant improvements in operational efficiency, product quality, and customer satisfaction. The study also discusses the broader implications of these findings, including the necessity for businesses to adopt modern, agile deployment strategies to stay competitive and responsive in the digital landscape. This research contributes to the understanding of how automated deployment strategies, underpinned by CI/CD and DevOps practices, can revolutionize software development processes. It provides valuable insights for organizations looking to enhance their software deployment methodologies, ultimately leading to improved business

关键词： DevOps Production Organizations Manuals Information age Software Stability analysis

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MediGlove: Integrating IoT and Machine Learning for Personalized Remote Health Care

MediGlove: Integrating IoT and Machine Learning for Personal...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： K V Nanda T G Keerthan Kumar N Achyutha Prasad M L Raviprakash J Anand Babu M R Naveen Kumar Master of Computer Applications Sri Siddhartha Institute of Technology Tumkur India Information Science and Engineering Siddaganga Institute of Technology Tumkur India Computer Science and Engineering East West Institute of Technology Bangalore India Artificial Intelligence and Machine Learning Kalpataru institute of technology Tiptur India Information Science and Engineering Malnad College of Engineering Hassan India Information Science and Engineering Vidyavardhaka College of Engineering Mysore India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Mobile operating systems have advanced significantly in recent decades, with Android emerging as a widely used open-source platform. Simultaneously, the Internet of Things (IoT) has transformed computer equipment by enabling seamless data transfer over networks without human involvement. This study makes use of intelligent gloves equipped with various sensors, as well as IoT and Android applications, to improve healthcare. In this work, we introduce a pioneering framework that combines IoT and Android technology to revolutionize remote health monitoring called IoT-enabled Smart Gloves for Remote Health Monitoring using an Android-based solution approach (MediGlove). MediGlove uses intelligent smart gloves with robust sensors to continuously collect crucial health data. The solution works with IoT devices to send data quickly to a cloud-based server. The cloud server provides real-time health metric analysis with great precision by utilizing cutting-edge algorithms and machine learning techniques. A simple Android software acts as a dynamic health dashboard, allowing users to track their health in real-time. The framework generates real-time alerts for problematic health markers, allowing prompt medical action. Extensive testing validates the system’s efficacy and dependability in health monitoring and the seamless integration of smart gloves with Android applications.

关键词： Cloud computing Operating systems Medical services Machine learning Real-time systems Internet of Things Servers

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Dynamic Network Bandwidth Optimization and Its Application to Underwater Fibre Optic Communication 1

Dynamic Network Bandwidth Optimization and Its Application t...

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

作者： Singh, Vaishali Sudha, D. Srivastava, Manish Das, Sidhant Naveena, S. Sathiya Bhandari, Mahesh A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350359756

Dynamic community Bandwidth Optimization and Its utility to Underwater Fiber Optic communique is a studies topic focused on enhancing the transmission competencies of submerged fiber-optic networks. The primary aim is to design a dynamic bandwidth allocation algorithm able to acquiring most efficient transmission overall performance given the underwater channel and conversation necessities. To accomplish that, the set of rules have to be able to update the to be had channel bandwidth depending on the utility's call for it whilst additionally keeping safety and privacy. The resulting set of rules should be relevant to an extensive range of underwater fiber-optic systems. Furthermore, the solution must be flexible sufficient to be used for both asynchronous and synchronous packages, making an allowance for greater green utilization of the total bandwidth to be had. Moreover, the studies ought to awareness on finding alternative techniques for growing the transmission capacities of a given fiber-optic network which will accommodate accelerated visitors or new conversation necessities. These can include methods which include coding, dynamic modulation, and equalization. In end, dynamic community bandwidth optimization and its software to underwater fiber-optic communication is a complicated research region, however the resulting answer may want to revolutionize underwater communique networks. © 2023 IEEE.

关键词： Bandwidth

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APE-GAN++: An Improved APE-GAN to Eliminate Adversarial Perturbations

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第3期48卷 1-18页

作者： Yang, Rui Chen, Xiu-Qing Cao, Tian-Jie Master's student of the School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu China Lecturer of the School of Medicine Information and Engineering Xuzhou Medical University XuzhouJiangsu China Professor of the School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu China

Deep neural networks (DNNs) have been deployed successfully in various scenarios, but numerous studies have shown that deep neural networks are vulnerable to the attack of adversarial examples. In order to protect deep neural networks against adversarial examples, a lot of countermeasures have been developed. The APE-GAN is one of these proposed countermeasures, which employ a generative adversarial network (GAN) to eliminate adversarial perturbations. Although it performs more excellently than other countermeasures, it still has some shortcomings. First, its training process is precarious and has a vanishing gradient problem. Second, its performance can be improved further. In this paper, we propose the APE-GAN++, which is an enhanced APE-GAN, to overcome its disadvantages. First, the proposed APE-GAN++ utilizes the WGAN-GP loss to make the training process stable. Then, it uses a newly added third-party classification loss to enhance the capacity of the generator to eliminate adversarial perturbations. Experiments are conducted on the MNIST and CIFAR-10 datasets to verify the proposed APE-GAN++'s performance. Experimental results show that the proposed APE-GAN++ has a stable training process and solves the vanishing gradient problem. Besides, it can also achieve a more excellent performance than other countermeasures when defending against adversarial examples. Experimental code is available at https://***/Afreadyang/APE-GAN-Plus-Plus. © 2021. All Rights Reserved.

关键词： Generative adversarial networks

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Preliminary Evaluation of Kyokko for Inter-FPGA Communication Framework CIRCUS

Preliminary Evaluation of Kyokko for Inter-FPGA Communicatio...

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Kaito Kitazume Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

In recent years, Field-Programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz... 详细信息

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

关键词： Conferences High performance computing Circuits Cluster computing Low latency communication Field programmable gate arrays

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Enhancing Customer Experience in Retail for Adaptive Store Layouts with Reinforcement Learning

Enhancing Customer Experience in Retail for Adaptive Store L...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： P. Suresh T Sivakumar N. Revathy M. Pandi Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Master of Computer Applications Hindustan College of Arts and Science Coimbatore Tamil Nadu India Department of Information Technology SRM Madurai College for Engineering and Technology Sivagangai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the competitive retail industry, improving customer experience is essential for increasing sales and cultivating brand loyalty. A crucial determinant of consumer happiness is the shop layout, which impacts customers' navigation, exploration, and interaction with merchandise. Traditional store layouts are often inflexible and do not adjust to evolving customer behaviour, hence diminishing their efficacy in enhancing the shopping experience. This paper examines the use of Reinforcement Learning (RL) to create adaptable shop layouts that adjust based on real-time consumer interactions. Through the analysis of customer behaviour and feedback, RL algorithms may dynamically modify the layout of merchandise, aisles, and promotional areas, therefore optimizing the retail environment for both consumers and merchants. The RL algorithm analyses historical data, progressively optimizing store layouts to correspond with consumer preferences, so improving the entire shopping experience. It provides substantial advantages, such as enhanced consumer involvement, more operational efficiency, and elevated revenue designs. It examines the potential of RL in revolutionizing retail settings in optimizing adaptable shop layouts and its effects on consumer happiness and corporate performance.

关键词： Technological innovation IEEE merchandise Heuristic algorithms Visual analytics Layout Reinforcement learning Real-time systems Internet of Things Artificial intelligence Optimization

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