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Artificial Intelligence has transcended from a buzzword to a vital tool in business and personal applications. As the AI field grows, so…
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In the era of Artificial Intelligence (AI), extracting meaningful knowledge from vast datasets has become critical for both businesses and…
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Alright, you tech-savvy human, brace yourself for a thrilling adventure into the land of artificial intelligence! We’re not just dipping…
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This article provides a comprehensive guide on deploying a large language model (LLM) application using the PAI-EAS (Platform for AI —…
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Are you ready to dive into the world of Generative AI (GenAI) and witness the magic of Large Language Models (LLM)? Meet Alibaba Cloud…
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Artificial Intelligence (AI) Painting or AI Generative Content (AIGC) is a fascinating field within artificial intelligence that focuses on creating visual content using advanced machine learning techniques. These areas of research and development have gained significant attention in recent years as they offer innovative and creative solutions for generating artwork, graphics, and other visual media. One of the foundation models for AIGC is the diffusion model.
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Generative AI (GenAI) models have revolutionized the way we create and experience content. These models possess the remarkable ability to…
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Compute Nest is a comprehensive cloud integration Platform-as-a-Service (PaaS) solution designed for Independent Software Vendors (ISVs)…
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Explore the integration of AnalyticDB for PostgreSQL with large language models on Alibaba Cloud’s PAI platform, empowering businesses…
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Alibaba Cloud is a cloud computing provider that offers a wide range of cloud products and services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)
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Managing a delivery business can be challenging, with many tasks that require constant attention. From managing drivers to tracking…
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We recommend using ECS for the backend and front end. If the user will use open-source, the LLM (Large Language Model) can be used GPU-ECS…
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Running a delivery business can be a challenging task. From managing your drivers to tracking your deliveries, there are many aspects that…
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Generative AI models coupled with relevant text/image data can be used to oeuvre arts, poems, and creative programming codes
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Kubernetes is an open-source container-orchestration system that enables teams to deploy, scale, and manage containerized applications. It…
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Previously, we prepared an ECS instance and Docker environment at the ECS instance here and here. Now it’s time to run the Machine Learning…
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Tensorflow runs in the docker and GPU within docker
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Nowadays, we have an extremely high-speed changing live environment. Most changes come because of Artificial Intelligence (AI).
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The stochastic — means a system or a process that is linked with a random probability. Hence, in Stochastic Gradient Descent, a few…
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Applied Data Science Capstone
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Artificial intelligence (AI) has the potential to help solve some of the world’s most intractable international development problems. From…
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Nowadays, deep learning can solve a lot of challenging problems quickly and with a few codes. But underestimating the constraints and…
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Intro: what a data scientist is and why 93% of companies need a data scientist In the 21st century, IT development is critical for…
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I started an AI course with unpack.AI, and fast.AI The Deep Learning for Coders is a fantastic course from fast.ai. The authors explained…
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Participated as an Organizing Committee member in the Intelligent Rail System Transportation Summit, contributing to the event’s organization and discussions on intelligent rail transportation systems.
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Implemented a solution at L-Cube LLC to optimize the performance of their departmental network, enhancing communication efficiency and reliability.
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Developed an intelligent system for reading and identifying ISO 6346 container codes in logistics ports, enhancing access control and efficiency.
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Created a model for estimating the performance of communication protocols for channels with noise and simulated it using Matlab Communication Toolbox. The simulation results and implemented models on the EMC 2 data center were compared, showing deviations within 11%, confirming the research’s consistency.
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Moscow City Project involves integrating smart systems across five different office building groups with varied requirements. The project includes computer programming for central control and night light adjustment, managing over 800 square meters with 15,000 signals from different controllers.
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This project aims to classify the accents of English language speakers using audio recordings. By leveraging machine learning models such as FFNN, CNN, and LSTM, the system can accurately identify accents, aiding in language learning and accent improvement.
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Developed a face recognition system for smart building and parking projects in Xiamen city, China, resulting in the successful implementation of numerous projects.
Published in ransport: Problems, ideas and perspectives. Science Week, 2013
Recommended citation: Farruh Kushnazarov, "Decoding information flows by Viterbi." ransport: Problems, ideas and perspectives. Science Week, 2013. https://biblioserver.usurt.ru/cgi-bin/irbis64r_13/cgiirbis_64.exe?LNG=&C21COM=S&I21DBN=TFZHT&P21DBN=TFZHT&S21FMT=fullwebr&S21ALL=%3C.%3EK%3D%D0%B4%D0%B5%D0%BA%D0%BE%D0%B4%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5%3C.%3E&FT_REQUEST=&FT_PREFIX=&Z21ID=&S21STN=1&S21REF=5&S21CNR=10
Published in Сборник трудов VII научно-практической конференции молодых ученых, 2015
Recommended citation: Фаррух Кушназаров, Одилджан Турдиев, Фирдавс Кушназаров, "ЭФФЕКТИВНОСТЬ ЦИКЛИЧЕСКИХ КОДОВ ПРИ ПЕРЕДАЧЕ ДАННЫХ В КАНАЛАХ СВЯЗИ С ПОМЕХАМИ." Сборник трудов VII научно-практической конференции молодых ученых, 2015. mr.ifmo.ru/files/Sborniki/sbornik_2016.pdf
Published in Transport: Problems, ideas and perspectives. Science Week - 2015, 2015
Recommended citation: Farruh Kushnazarov, "Analysis of interference in the communication channels to the real speed of data transmission." Transport: Problems, ideas and perspectives. Science Week - 2015, 2015. https://biblioserver.usurt.ru/cgi-bin/irbis64r_13/cgiirbis_64.exe?LNG=en&C21COM=S&I21DBN=TFZHT&P21DBN=TFZHT&S21FMT=infow_wh&S21ALL=&Z21ID=&S21SRW=&S21SRD=&S21STN=1&S21REF=1131010&S21CNR=20&FT_REQUEST=%D0%90%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7%20%D0%B2%D0%BB%D0%B8%D1%8F%D0%BD%D0%B8%D1%8F%20%D0%BF%D0%BE%D0%BC%D0%B5%D1%85%20%D0%B2%20%D0%BA%D0%B0%D0%BD%D0%B0%D0%BB%D0%B0%D1%85%20%D1%81%D0%B2%D1%8F%D0%B7%D0%B8%20%D0%BD%D0%B0%20%D1%80%D0%B5%D0%B0%D0%BB%D1%8C%D0%BD%D1%83%D1%8E%20%D1%81%D0%BA%D0%BE%D1%80%D0%BE%D1%81%D1%82%D1%8C%20%D0%BF%D0%B5%D1%80%D0%B5%D0%B4%D0%B0%D1%87%D0%B8%20%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85%20%D0%BA%D1%83%D1%88%D0%BD%D0%B0%D0%B7%D0%B0%D1%80%D0%BE%D0%B2&FT_PREFIX=K%3D
Published in Proceeding of Petersburg Transport University, 2015
Recommended citation: Farruh Kushnazarov, Valentin Yakovlev, Odilzhan Turdiyev, "Comparison of the performance of protocols of access to cloud resources." Proceeding of Petersburg Transport University, 2015. http://izvestiapgups.org/assets/files/10.20295-1815-588X-2015-4/10.20295-1815-588X-2015-4-117-123.pdf
Published in Intellectual Technologies on Transport., 2015
Recommended citation: Farruh Kushnazarov, "Controlling data flows in the noisy channels." Intellectual Technologies on Transport., 2015. http://itt-pgups.ru/issue/download/151/17
Published in Proceeding of Petersburg Transport University, 2015
Recommended citation: Valentin Yakovlev, Farruh Kushnazarov, "Evaluation of the effect of interferences on link-layer protocol performance." Proceeding of Petersburg Transport University, 2015. http://izvestiapgups.org/archive/2015/2015-1.html
Published in Proceeding of Petersburg Transport University, 2015
Recommended citation: Valentin Yakovlev, Farruh Kushnazarov, "Model of repeated channel transmission, against interference." Proceeding of Petersburg Transport University, 2015. http://www.nauteh-journal.ru/files/05120b85-ba16-435d-8835-0e2d3b612410
Published in Prace Naukowe Politechniki Warszawskiej. Transport, 2016
Recommended citation: Farruh Kushnazarov, Firdavs Kushnazarov, "Analysis of the data transmission in communication channels with noise." Prace Naukowe Politechniki Warszawskiej. Transport, 2016. https://www.wt.pw.edu.pl/content/download/6398/35819/file/Farruh%20I%20Kushnazarov,%20Firdavs%20I%20Kushnazarov.pdf
Published in 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017
Recommended citation: Farruh Kushnazarov, "Analysis of perfomance of algorithms for scoring system in organization of customer profiles." 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8078680
Published in Proceedings of the FRUCT’18, 2017
Recommended citation: Farruh Kushnazarov, Valentin Yakovlev, "Control of exchange messages in a noisy channel." Proceedings of the FRUCT’18, 2017. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7561561
Published in Intellectual Technologies on Transport, 2018
Recommended citation: Farruh Kushnazarov, Babina G., "Directions of Development of the Corporate Information System of the Company «Uzbekiston Temir Yullari»." Intellectual Technologies on Transport, 2018. http://itt-pgups.ru/issue/download/182/76
Published in 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2018
Recommended citation: Farruh Kushnazarov, "Throughput of communication protocols for distributed systems transferring a group of frames under noise." 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2018. https://ieeexplore.ieee.org/document/8386554
Published in Chapters, 2019
Recommended citation: Farruh Kushnazarov, "Consumer Life Cycle and Profiling: A Data Mining Perspective." Chapters, 2019. https://www.intechopen.com/books/consumer-behavior-and-marketing/consumer-life-cycle-and-profiling-a-data-mining-perspective
Published in 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2019
Recommended citation: Farruh Kushnazarov, "Data Stream Controlling in Communication Channels with Noise." 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2019. https://ieeexplore.ieee.org/abstract/document/8725672
Published in Chinese Journal of Refrigeration Technology/制冷技术, 2021
Recommended citation: Dan ZHAO, Bo FAN, FARRUH Kushnazarov, "Anomaly Detection of Unitary Air Conditioners Based on Isolation Forest Method/基于孤立森林方法的单元式空调器异常检测." Chinese Journal of Refrigeration Technology/制冷技术, 2021. https://scjg.cnki.net/kcms/detail/detail.aspx?filename=ZLJS202103007&dbcode=CJFQ&dbname=CJFD2021&v=
Published in E Conference Zone, 2022
Recommended citation: Kushnazarov Isakulovich, Nosirov o’g’li, "TEXT SUMMARIZATION VIA DEEP LEARNING." E Conference Zone, 2022. https://www.econferencezone.org/index.php/ecz/article/view/142
Published in Journal of Building Engineering, 2022
Use Google Scholar for full citation
Recommended citation: Jingming Li, Nianping Li, Bao Yue, Rui Yan, Farruh Kushnazarov, Anbang Li, Kehua Li, "Research on the semantic web representation for building operation with Variable Refrigerant Flow systems." Journal of Building Engineering, 2022.
Published in E3S Web of Conferences, 2023
Use Google Scholar for full citation
Recommended citation: Matkarim Ibragimov, Firdavs Kushnazarov, Farruh Kushnazarov, "The use of a pulse-expansion module to increase the efficiency of natural feeding of fish by flying insects on artificial ponds." E3S Web of Conferences, 2023.
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The human brain is a biologically inspired class of algorithms that process the information network of primitive computational elements. Deep learning is a useful technology to build a multilayer neural network architectures and their applications in a broad class of problems. Deep learning is used for speech recognition, image analysis, video analysis, and behavior prediction text processing machine translation. Used static data processing has several disadvantages, like high labor costs, for developing specialized data processing systems. The deep learning achieves low labor costs due to the algorithms’ versatility, the ease of adaptation when you change the structure of the data, more data volume, and reliable data.
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In particular, the seminar discussed the technical parameters and trends in the production of cars driven without human intervention, the introduction of information and communication technologies in the automotive industry, in the ongoing scientific research and changes in the automotive industry. This event was organized interactively and aroused great interest among students.
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Nowadays, we can see the implementation of high-speed machine learning in all areas of society, such as trading, factoring, security, services, and others. We can see different examples of AI/ML’s implementation in journal publications, on TV shows, and on the Internet every day. We will analyze the pitfalls and possible obstacles in the implementation of AI/ML in BL through different examples.
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The volume of information is increasing every day. Despite this, Big Data functioning all over the world is only an insignificant part of it. According to IDC forecasts, the volume of information may reach 40 Zettabytes by 2020. From the beginning of 2010 to the present, the amount of data has increased 50-55 times. According to research, 2.8 Zettabytes of information currently exist and are processed. In his talk on Big Data, he will highlight fundamental issues in this area.
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Speech about what DL and ML are and how to use these technologies in the interests of contemporary life. And also about artificial neural networks based on CNN (Convolutional NN) and RNN (Recurrent NN) technologies, etc.
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Customer lifetime value is the metric that indicates the total revenue a business can reasonably expect from a single customer account. It considers a customer’s revenue value and compares that number to the company’s predicted customer lifespan. Businesses use this metric to identify significant customer segments that are the most valuable to the company.
Undergraduate course, Tashkent State Transport University, Information systems and technologies in transport, 2010
To conduct laboratory and practical classes for below subjects:
Workshop, Emperor Alexander I St.Petersburg State Transport University, Information and computing systems, 2014
During the Ph.D. study had amazing experience of assist professor and conduct below subjects:
Undergraduate course, Tashkent State Transport University, Department, 2016
To conduct laboratory and practical classes for below subjects:
Undergraduate course, Tashkent University of Information Technologies, Multimedia technologies, 2020
To conduct laboratory and practical classes for below subjects: