Category Archives: Data Mining
Principles of Data Science. Explore the fundamentals, techniques … – DataDrivenInvestor
Explore the fundamentals, techniques, and future trends in data science.Photo by Alex wong on Unsplash
Table of Contents1. Understanding Data Science1.1. What is Data Science?1.2. Role of Data Science in Todays World1.3. Key Components of Data Science1.4. Different Fields in Data Science
2. Fundamental Concepts of Data Science2.1. Basics of Statistics for Data Science2.2. Machine Learning Algorithms2.3. Importance of Data Cleaning2.4. Understanding Data Visualization2.5. Introduction to Predictive Analytics2.6. Understanding Big Data
3. Implementing Data Science3.1. Essential Tools for Data Science3.2. The Data Science Process3.3. Best Practices in Data Science3.4. Real World Applications of Data Science3.5. Future Trends in Data Science
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from various forms of data, both structured and unstructured. It is fundamentally about understanding and interpreting complex and large sets of data. By leveraging statistical analysis, data engineering, pattern recognition, predictive analytics, data visualization, among others, data science helps to make sense of massive data volumes, allowing individuals and organizations to make more informed decisions. Moreover, data science plays a crucial role in todays information-driven world, where data is a key resource. Understanding the principles of data science provides the groundwork for diving into this dynamic field.
Data Science is an interdisciplinary field that uses scientific methods, processes, and systems to glean insights from structured and unstructured data. It integrates statistical, mathematical, and computational techniques to interpret, manage, and use data effectively. Data science is not just about analyzing data, but it also involves understanding and translating data-driven insights into actionable plans. The goal of data science is to create value from data, which can help individuals, businesses, and governments make data-driven decisions. It is a crucial field in the modern world where data is continuously generated and consumed, impacting every sector, from healthcare to finance, marketing, and beyond.
The role of data science in todays world is incredibly diverse and pervasive. In business, data science techniques are used to understand customer behavior, optimize operations, and improve products and services. In healthcare, it helps in predicting disease trends and improving patient care. Governments use data science to formulate policies, provide public services, and improve governance. It also plays a crucial role in emerging technologies such as artificial intelligence and machine learning. Data science helps to handle the vast amount of data produced daily and draw meaningful insights from it. In essence, data science has become integral to our society, transforming the way we live, work, and make decisions.
Data Science comprises several key components that help it function effectively. These components include: 1. Data: The basis of any data science project is the raw data, which can be structured or unstructured. 2. Statistics & Probability: These mathematical disciplines allow a data scientist to create models, make predictions and understand data. 3. Programming: Languages like Python and R are essential for data cleaning, data manipulation, and implementing algorithms. 4. Machine Learning: This is used to create and apply predictive models based on the data. 5. Data Visualization: This involves creating visual representations of data to make complex patterns clear and understandable. 6. Domain Knowledge: Understanding the domain to which the data pertains is crucial for interpreting results and making accurate predictions.
Data science is a broad field that intersects with many disciplines. These include Machine Learning, where algorithms are used to learn from data and make predictions; Data Mining, which involves extracting valuable information from vast datasets; Predictive Analytics, where historical data is used to predict future trends; Data Visualization, which transforms complex data into visual, easy-to-understand formats; and Big Data Analytics, which handles extremely large data sets. Other fields include Natural Language Processing (NLP), which allows computers to understand human language, and Computer Vision, where machines interpret visual data. These diverse fields collectively contribute to the extensive potential of data science.
Statistics is a cornerstone of data science. It provides the tools to understand patterns in the data and to make predictions about future events. The basic concepts in statistics every data scientist should know include Descriptive Statistics, where you summarize and describe the main features of a data set; Inferential Statistics, which allows you to make inferences about a population based on a sample; Probability Distributions, which depict the likelihood of all possible outcomes of a random event; Hypothesis Testing, a method to make decisions using data; and Regression Analysis, a statistical tool for investigating the relationship between variables. Understanding these fundamental statistical concepts is essential in interpreting data and building effective data science models.
Machine Learning (ML) algorithms are a vital part of data science, allowing computers to learn from data. ML algorithms can be broadly categorized into supervised learning, where the algorithm is trained on a labeled dataset; unsupervised learning, which deals with unlabeled data; and reinforcement learning, where an agent learns to perform actions based on rewards and punishments. Key algorithms include linear regression and logistic regression, decision trees, support vector machines, and neural networks. More advanced techniques involve ensemble methods, deep learning, and reinforcement learning. Knowledge of these algorithms, their applications, strengths, and limitations are crucial for any data scientist. They form the backbone of data-driven predictions and decision making in various fields.
Data cleaning, also known as data cleansing or data preprocessing, is a critical step in the data science process. It involves identifying and correcting errors in the data, dealing with missing values, and ensuring that the data is consistent and in a suitable format for analysis. The importance of data cleaning lies in the fact that the quality of data directly impacts the accuracy and reliability of machine learning models and statistical analysis. Poorly prepared or unclean data can lead to misleading results and erroneous conclusions. Therefore, data cleaning is an essential step to ensure the integrity of the analysis, create accurate models, and ultimately drive sound, data-driven decisions.
Data visualization is the graphical representation of data. It involves producing images that communicate relationships among the represented data to viewers of the images. This is an important aspect of data science as it enables the communication of complex data in a form that is easy to understand and interpret. It helps to convey insights and findings in a visual format, making it easier for others to understand the significance of data patterns or trends. Effective data visualization can significantly aid in making data-driven decisions and can serve as a powerful tool to communicate the results of a data science project. Tools like Matplotlib, Seaborn, and Tableau are commonly used for creating compelling and meaningful visualizations.
Predictive Analytics is an area of data science that uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal of predictive analytics is to go beyond what has happened and provide the best assessment of what will happen in the future. It can be used in various fields, including finance, healthcare, marketing, and many others, for forecasting trends, understanding customer behavior, and risk management. Predictive models capture relationships among various data elements to assess risk with a particular set of conditions. These models can be constantly refined and modified as additional data is fed into them, improving their predictive accuracy over time. Thus, predictive analytics is a powerful tool in the data science arsenal.
Big Data refers to massive volumes of data that cant be processed effectively with traditional applications. The term is often associated with the three Vs: Volume (vast amounts of data), Variety (different types of data), and Velocity (speed at which data is produced and processed). The data can come from various sources such as social media, business transactions, or machines and sensors. Understanding Big Data involves not only managing and storing large data sets but also extracting valuable insights from this data using various data analysis and machine learning techniques. Big Data has enormous potential and is a fundamental aspect of modern data science.
There are numerous tools available for implementing data science effectively. These include programming languages such as Python and R, which are extensively used for data manipulation, statistical analysis, and machine learning. SQL is essential for handling and querying databases. For data cleaning and manipulation, tools like Pandas and dplyr are popular. When it comes to machine learning, Scikit-learn, TensorFlow, and Keras are widely used. Jupyter notebooks are handy for interactive coding and data analysis. For visualization, Matplotlib, Seaborn, and Tableau are excellent tools. Finally, for handling big data, Hadoop and Spark are key. Besides, cloud platforms like AWS, Google Cloud, and Azure offer services to handle, store, and analyze massive datasets. Familiarity with these tools can significantly improve a data scientists productivity and effectiveness.
The Data Science process involves a series of steps that guide the extraction of meaningful insights from data. It generally starts with defining the problem and understanding the domain. Then comes data collection, where relevant data is gathered from various sources. The collected data is then cleaned and preprocessed to remove any errors or inconsistencies. Exploratory Data Analysis (EDA) follows, which involves understanding the patterns and relationships in the data through statistical analysis and data visualization. The next step is to create machine learning models based on the insights gained from EDA. These models are trained, tested, and optimized for accuracy. The final step is communicating the results and deploying the model for real-world use. This process ensures a structured approach to tackling data science problems.
Data science is a complex field, and its crucial to follow best practices to ensure successful outcomes. First and foremost, always understand the problem and the data before diving into analysis or modeling. Regularly conduct exploratory data analysis to uncover patterns, spot anomalies, and gain insights. Ensure data quality by spending ample time in the data cleaning phase, as quality data is essential for building accurate models. Use appropriate machine learning algorithms based on the problem at hand and remember, complex models are not always better. Always validate your models using proper methods like cross-validation. Practice ethical data science by respecting privacy and ensuring transparency in your models. Lastly, effectively communicate your findings to all stakeholders, not just technical ones, as data science is valuable only when its results can be understood and used.
Data science has a vast array of real-world applications, revolutionizing industries and sectors. In healthcare, data science is used for disease prediction, drug discovery, and patient care improvement. In finance, it aids in risk assessment, fraud detection, and investment predictions. Retail businesses leverage data science for inventory management, customer segmentation, and personalized marketing. It plays a key role in improving customer experiences through recommendation systems in companies like Netflix and Amazon. In transportation, it optimizes routes and improves logistics. Data science also aids in predicting equipment failures and enhancing safety measures in the manufacturing sector. Furthermore, in the public sector, it helps make data-driven policies and improves public services. With continuous advancements in technology, the application of data science is only set to grow across various domains.
The future of data science promises exciting trends and advancements. As more industries recognize the value of data-driven decisions, demand for skilled data scientists will continue to rise. AI and machine learning will further integrate into businesses, automating routine tasks and improving efficiency. The importance of ethics in AI will increase, focusing on areas like transparency, interpretability, and fairness in machine learning models. We can expect more advancements in tools and platforms for handling big data, improving the ability to store, process, and analyze large datasets. There will be increased use of real-time analytics as businesses seek immediate insights to respond swiftly to changes. Moreover, advancements in quantum computing and edge computing may redefine computational limits in data science. These trends will shape the future landscape of data science, creating new opportunities and challenges.
Visit link:
Principles of Data Science. Explore the fundamentals, techniques ... - DataDrivenInvestor
Smart Mining Market Prolific Business Methodology and Techniques … – The Bowman Extra
New Jersey, United StatesThe GlobalSmart MiningMarket is expected to grow with a CAGR of %, during the forecast period 2023-2030, the market growth is supported by various growth factors and major market determinants. The market research report is compiled by MRI by conducting a rigorous market study and includes the analysis of the market based on segmenting geography and market segmentation.
Moreover, the rising awareness about the benefits of Smart Mining, including improved efficiency, cost savings, and sustainability, is fostering market growth. Businesses across different sectors are recognizing the value of Smart Mining in streamlining operations, reducing environmental impact, and enhancing overall productivity.
Download a PDF Sample of this report: https://www.marketresearchintellect.com/download-sample/?rid=196105
The market study was done on the basis of:
Region Segmentation
Product Type Segmentation
Application Segmentation
MRI compiled the market research report titled GlobalSmart MiningMarket by adopting various economic tools such as:
Company Profiling
Request for a discount on this market study: https://www.marketresearchintellect.com/ask-for-discount/?rid=196105
To conduct a market study in-depth, MRI adopted various market research tools and followed a traditional research methodology is one of them, data and other qualitative parameters were analyzed by adopting primary and secondary research methodologies, which were explained in detail, as follows:
Primary Research
In the primary research process, information was collected on a primary basis by:
Basic information details were collected to collect quantitative and qualitative data, based on different market parameters, the data was organized and analyzed from both the demand and supply sides of the market.
Secondary Research
For secondary research, various authentic web sources and research papers/white papers were considered to identify and collect information and market trends. The data collected from secondary sources help to calculate the pricing models, and business models of various companies along with current trends, market sizing, and company initiatives. Along with these open-available sources, the company also collects information from various paid databases that are extensive in terms of information in both qualitative and quantitative manner.
Research by other methods:
MRI follows other research methodologies along with traditional methods to compile the 360-degree research study that is majorly customer-focused and involves a major company contribution to the research team. The client-specific research provides the market sizing forecast and analyzed the market strategies that are focused on client-specific requirements to analyze the market trends, and forecasted market developments. The companys estimation methodology leverages the data triangulation model that covers the major market dynamics and all supporting pillars. The detailed description of the research process includes data mining is an extensive step of research methodology. It helps to obtain the information through reliable sources. The data mining stage includes both primary and secondary information sources.
The report Includes the Following Questions:
About Us: Market Research IntellectMarket Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.
Contact Us: Mr. Edwyne FernandesMarket Research IntellectNew Jersey (USA)US: +1 (650)-781-4080 USToll-Free: +1 (800)-782-1768Website: -https://www.marketresearchintellect.com/
Excerpt from:
Smart Mining Market Prolific Business Methodology and Techniques ... - The Bowman Extra
This start-up says it can use discarded crypto mining rigs to train AI … – Tech Monitor
Distributed computing start-up Monster API believes it can deploy unused cryptocurrency mining rigs to meet the ever-growing demand for GPU processing power. The company says its network could be expanded to take in other devices with spare GPU capacity, potentially lowering the cost of developing and accessing AI models.
GPUs are often deployed to mine cryptocurrencies such as Bitcoin. The mining process is resource-intensive and requires a high level of compute, and at peak times in the crypto hype cycle, this has led to a shortage of GPUs on the market. As prices rocketed, businesses and individuals turned to gaming GPUs produced by Nvidia, which they transformed into dedicated crypto-mining devices.
Now interest in crypto is waning, many of these devices are gathering dust. This led Monster APIs founder Gaurav Vij to realise they could be re-tuned to work on the latest compute intensive trend training and running foundation AI models.
While these GPUs dont have the punch of the dedicated AI devices deployed by the likes of AWS or Google Cloud, Gaurav says they can train optimised open source models at a fraction of the cost of using one of the cloud hyperscalers, with some enterprise clients finding savings of up to 80%.
The machine learning world is actually struggling with computational power because the demand has outstripped supply, says Saurabh Vij, co-founder of Monster API. Most of the machine learning developers today rely on AWS, Google Cloud, Microsoft Azure to get resources and end up spending a lot of money.
As well as mining rigs, unused GPU power can be found in gaming systems like the PlayStation 5 and in smaller data centres. We figured that crypto mining rigs also have a GPU, our gaming systems also have a GPU, and their GPUs are becoming very powerful every single year, Saurabh told Tech Monitor.
Organisations and individuals contributing compute power to the distributed network go through an onboarding process, including data security checks. The devices are then added as required, allowing them to expand and contract the network based on demand. They are also given a share of the profit made from selling the otherwise idle compute power.
While reliant on open-source models, Monster API could build its own if communities funding new architectures lost support. Some of the biggest open-source models originated in a larger company or major lab, including OpenAIs transcription model Whisper and LLaMa from Meta.
Saurabh says the distributed compute system brings down the cost of training a foundation model to a point where in future they could be trained by open-source and not-for-profit groups and not just the large tech companies with deep pockets.
If it cost $1m to build a foundational model, it will only cost $100,000 on a decentralised network like us, Saurabh claims. The company is also able to adapt the network so that a model is trained and run within a specific geography, such as the EU to comply with GDPR requirements on data transmission across borders.
Monster API says it also now offers no-code tools for fine-tuning models, opening access to those without technical expertise or resources to train models from scratch, further democratising the compute power and access to foundation AI.
Fine-tuning is very important because if you look at the mass number of developers, they dont have enough data and capital to train models from scratch, Saurabh says. The company says it has cut fine-tuning costs by up to 90% through optimisation, with fees around $30 per model.
While regulation looms for artificial intelligence companies, which could directly impact those training models and open source, Saurabh believes open-source communities will push back against overreach. But Monster API says it recognises the need for managing risk potential and ensuring traceability, transparency and accountability across its decentralised network.
In the short term, maybe regulators would win but I have very strong belief in the open source community which is growing really really fast, says Saurabh. There are twenty-five million registered developers on [API development platform] Postman and a very big chunk of them are now building in generative AI which is opening up new businesses and new opportunities for all of them,
With low-cost AI access, Monster API says the aim is to empower developers to innovate with machine learning. They have a number of high-profile models like Stable Diffusion and Whisper available already, with fine-tuning accessible. But Saurabh says they also allow companies to train their own, from scratch foundation models using otherwise redundant GPU time.
The hope is that in future they will be able to expand the amount of accessible GPU power beyond just the crypto rigs and data centres. The aim is to provide software to bring anything with a suitable GPU or chip online. This could include any device with an Apple M1 or later chip.
Internally we have experimented with running stable diffusion on Macbook here, and not the latest one, says Saurabh. It delivers at least ten images per minute throughput. So thats actually a part of our product roadmap, where we want to onboard millions of Macbooks on the network. He says the goal is that while someone sleeps their Macbook could be earning them money by running Stable Diffusion, Whisper or another model for developers.
Eventually it will be Playstations, Xboxes, Macbooks, which are very powerful and eventually even a Tesla car, because your Tesla has a powerful GPU inside it and most of the time you are not really driving, its in your garage, Saurabh adds.
Continued here:
This start-up says it can use discarded crypto mining rigs to train AI ... - Tech Monitor
Cloud-based Database Market: Quantitative Analysis, Current and … – The Bowman Extra
New Jersey, United StatesThe GlobalCloud-based DatabaseMarket is expected to grow with a CAGR of %, during the forecast period 2023-2030, the market growth is supported by various growth factors and major market determinants. The market research report is compiled by MRI by conducting a rigorous market study and includes the analysis of the market based on segmenting geography and market segmentation.
Moreover, the rising awareness about the benefits of Cloud-based Database, including improved efficiency, cost savings, and sustainability, is fostering market growth. Businesses across different sectors are recognizing the value of Cloud-based Database in streamlining operations, reducing environmental impact, and enhancing overall productivity.
Download a PDF Sample of this report: https://www.marketresearchintellect.com/download-sample/?rid=282758
The market study was done on the basis of:
Region Segmentation
Product Type Segmentation
Application Segmentation
MRI compiled the market research report titled GlobalCloud-based DatabaseMarket by adopting various economic tools such as:
Company Profiling
Request for a discount on this market study: https://www.marketresearchintellect.com/ask-for-discount/?rid=282758
To conduct a market study in-depth, MRI adopted various market research tools and followed a traditional research methodology is one of them, data and other qualitative parameters were analyzed by adopting primary and secondary research methodologies, which were explained in detail, as follows:
Primary Research
In the primary research process, information was collected on a primary basis by:
Basic information details were collected to collect quantitative and qualitative data, based on different market parameters, the data was organized and analyzed from both the demand and supply sides of the market.
Secondary Research
For secondary research, various authentic web sources and research papers/white papers were considered to identify and collect information and market trends. The data collected from secondary sources help to calculate the pricing models, and business models of various companies along with current trends, market sizing, and company initiatives. Along with these open-available sources, the company also collects information from various paid databases that are extensive in terms of information in both qualitative and quantitative manner.
Research by other methods:
MRI follows other research methodologies along with traditional methods to compile the 360-degree research study that is majorly customer-focused and involves a major company contribution to the research team. The client-specific research provides the market sizing forecast and analyzed the market strategies that are focused on client-specific requirements to analyze the market trends, and forecasted market developments. The companys estimation methodology leverages the data triangulation model that covers the major market dynamics and all supporting pillars. The detailed description of the research process includes data mining is an extensive step of research methodology. It helps to obtain the information through reliable sources. The data mining stage includes both primary and secondary information sources.
The report Includes the Following Questions:
About Us: Market Research IntellectMarket Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.
Contact Us: Mr. Edwyne FernandesMarket Research IntellectNew Jersey (USA)US: +1 (650)-781-4080 USToll-Free: +1 (800)-782-1768Website: -https://www.marketresearchintellect.com/
View original post here:
Cloud-based Database Market: Quantitative Analysis, Current and ... - The Bowman Extra
Longtime faculty members retire this month | News – The College of New Jersey News
Posted on June 12, 2023
Five of TCNJs most beloved faculty members are retiring this month and while we wish them well, lets face it were already missing them.
Compte, professor of Spanish, joined the college in 1990 and has taught Spanish language courses, as well as senior seminar and graduate topics courses on Don Quixote. She served as acting dean of the School of Culture and Society (now known as the School of Humanities and Social Sciences) from 20012002 and as interim dean from 20082009.
Hirsh came to TCNJ in 2003 and has taught the physical chemistry sequence quantum chemistry and chemical thermodynamics in addition to general chemistry I and II.
Keep arrived in 2009 as dean of the School of Business, a post he held for nine years before serving as interim provost and vice president for academic affairs. He retires as professor of marketing, and remains a nationally recognized expert in multi-level marketing and pyramid schemes.
Ochs arrived at TCNJ in 2013, teaching courses in biostatistics for public health, data-mining, and statistical inference and probability, among others. He has also served as a mentor for independent research in math and stats, and as president of the campus chapter of Phi Beta Kappa.
Ruddy began her tenure in the psychology department in 1985, and has taught courses in biopsychology, psychopharmacology, developmental psychology, and research methods.
I will enjoy the freedom of retirement, but I will miss the warm interactions with my colleagues and, of course, the students, Compte said.
Keep echoed her sentiments. I will miss being part of TCNJ and the people who I came to enjoy and respect, he said. My 14 years at TCNJ were exceptionally gratifying and I feel fortunate to have been part of its mission.
Additional faculty members who retired earlier in the 20222023 academic year include Arthur Homuth, psychology; Mohamoud Ismail, sociology and anthropology; and Wei-Hong (Chamont) Wang, mathematics and statistics.
Emily W. Dodd 03
Visit link:
Longtime faculty members retire this month | News - The College of New Jersey News
Improved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV … – Nature.com
Tao, Y., Zongyang, Z., Jun, Z., Xinghua, C. & Fuqiang, Z. Low-altitude small-sized object detection using lightweight feature-enhanced convolutional neural network. J. Syst. Eng. Electron. 32(4), 841853 (2021).
Article Google Scholar
Chen, C., Liu, B., Wan, S., Qiao, P. & Pei, Q. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 22(3), 18401852 (2021).
Article Google Scholar
Yang, Z. Pedestrian detection for intelligent vehicle based on bilayer difference features algorithm. In International Conference on Transportation Information and Safety (ICTIS), 337340 (2015).
Prajwal, P., Prajwal, D., Harish, D. H., Gajanana, R., Jayasri, B. S. & Lokesh, S. Object detection in self driving cars using deep learning. In International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 17 (2021).
Wang, X., Bai, X., Liu, W. & Latecki, L. J. Feature context for image classification and object detection. In Computer Vision and Pattern Recognition Conference 2011. IEEE, 961968 (2011).
Kim, H., Lee, Y., Yim, B., Park, E. & Kim, H. On-road object detection using deep neural network. In International Conference on Consumer Electronics-Asia (ICCE-Asia), 14 (2016).
Byk, M., Duvar, R. & Urhan, O. Deep learning based vehicle detection with images taken from unmanned air vehicle. In Innovations in Intelligent Systems and Applications Conference (ASYU) 14 (2020).
Xu, Y., Yu, G., Wu, X., Wang, Y. & Ma, Y. An enhanced ViolaJones vehicle detection method from unmanned aerial vehicles imagery. IEEE Trans. Intell. Transp. Syst. 18(7), 18451856 (2017).
Article Google Scholar
Jonnalagadda, M., Taduri, S. & Reddy, R. RealTime traffic management system using object detection based signal logic. In Applied Imagery Pattern Recognition Workshop (AIPR) 15 (2020).
Zhang, X. & Zhu, X. Vehicle detection in the aerial infrared images via an improved Yolov3 network. In International Conference on Signal and Image Processing (ICSIP), 372376 (2019).
Avola, D. et al. A UAV video dataset for mosaicking and change detection from low-altitude flights. IEEE Trans. Syst. Man Cybern. Syst. 50(6), 21392149 (2020).
Article Google Scholar
Everingham, M., Van Gool, L., Williams, C. K., Winn, J. & Zisserman, A. The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303338 (2010).
Article Google Scholar
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Deva, Dollr, P. & Zitnick, C. L. Microsoft COCO: Common objects in context. In European conference on computer vision. Springer 740755 (2014).
Deng, J., Dong, W., Socher, R., Li, L. J., Kai, L. & Li, F. ImageNet: A large-scale hierarchical image database. Conference on Computer Vision and Pattern Recognition 248255 (2009).
Puri, D. COCO dataset stuff segmentation challenge. In International Conference On Computing, Communication, Control And Automation (ICCUBEA) 15 (2019).
Borji, A. Complementary datasets to COCO for object detection. In arXiv:2206.11473 (arXiv preprint) (2022).
Cao, K .Y. Cui, X. & Piao, J. C. Smaller target detection algorithms based on YOLOv5 in safety helmet wearing detection. IN International Conference on Robotics and Computer Vision (ICRCV) 154158 (2022).
Du, D., Zhu, P., Wen, L., Bian, X., Lin, H., Hu, Q., Peng, T., Zheng, J., Wang, X., Zhang, Y. & Zhang, L. VisDrone-DET2019: The vision meets drone object detection in image challenge results. In International Conference on Computer Vision Workshop (ICCVW) 213226 (2019).
Mueller, M., Smith, N. & Ghanem, B. A benchmark and simulator for UAV tracking. In European Conference on Computer Vision 445461 (Springer, 2016).
Liu, Z., Gao, G., Sun, L. & Fang, Z. HRDNet: High-resolution detection network for small objects. In International Conference on Multimedia and Expo (ICME) 16 (2021).
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q. & Ling, H. Detection and tracking meet drones challenge. arXiv:2001.06303 (arXiv preprint) (2020).
Xie, X. & Lu, G. A research of object detection on UAVs aerial images. In International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE) 342345 (2021).
Misra, D. Mish: A self regularized non-monotonic activation function. arXiv:1908.08681 (arXiv preprint) (2019).
Jana, A. P., Biswas, A. & Mohana, YOLO based detection and classification of objects in video records. In International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT) 24482452 (2018).
Gongguo, Z. & Junhao, W. An improved small target detection method based on Yolov3. International Conference on Electronics, Circuits and Information Engineering (ECIE) 220223 (2021).
Xu, Q. et al. Research on small target detection in driving scenarios based on improved Yolo network. IEEE Access 8, 2757427583 (2020).
Article Google Scholar
Mahendru, M. & Dubey, S. K. Real time object detection with audio feedback using Yolo vs. Yolov3. In International Conference on Cloud Computing, Data Science & Engineering (Confluence) 734740 (2021).
Rao, Y., Zhao, W., Tang, Y., Zhou, J., Lim, S. N. & Lu, J. Hornet: Efficient high-order spatial interactions with recursive gated convolutions. arXiv:2207.14284 (arXiv preprint) (2022).
Geng, Z. & Chen, G. A Novel real-time grasping method cobimbed with YOLO and GDFCN. In Joint International Information Technology and Artificial Intelligence Conference (ITAIC) 500505 (2022).
Xie, J., Pang, Y., Nie, J., Cao, J. & Han, J. Latent feature pyramid network for object detection. In IEEE Transactions on Multimedia 1 (2022).
Xing, H., Wang, S., Zheng, D. & Zhao, X. Dual attention based feature pyramid network. China Commun. 17(8), 242252 (2020).
Article Google Scholar
Bayhan, E., Ozkan, Z., Namdar, M. & Basgumus, A. Deep learning based object detection and recognition of unmanned aerial vehicles. In International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) 15 (2021).
Noori, M., Mohammadi, S., Majelan, G. S., Bahri, A. & Havaei, M. DFNet: Discriminative feature extraction and integration network for salient object detection. Eng. Appl. Artif. Intell. 89, 103419 (2020).
Article Google Scholar
Yang, T. & Tong, C. Small traffic sign detector in real-time based on improved YOLO-v4. In International Conference on High Performance Computing and Communications; 7th International Conference on Data Science and Systems; 19th International Conference on Smart City; 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application (HPCC/DSS/SmartCity/DependSys) 13181324 (2021).
Zhu, P. Convolutional neural networks based study and application for multicategory skin cancer detection. In International Conference on Electronic Communication and Artificial Intelligence (IWECAI) 558561 (2022).
zbay, M. & ahingil, M. C. A fast and robust automatic object detection algorithm to detect small objects in infrared images. In Signal Processing and Communications Applications Conference (SIU) 14 (2017).
Xing, C., Liang, X. & Yang, R. Compact one-stage object detection network. In International Conference on Computer Science and Network Technology (ICCSNT) 115118 (2020).
Luo, J., Yang, Z., Li, S. & Wu, Y. FPCB surface defect detection: A decoupled two-stage object detection framework. IEEE Trans. Instrum. Meas. 70, 111 (2021).
Google Scholar
Bai, T. Analysis on two-stage object detection based on convolutional neural networks. In International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE) 321325 (2020).
Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 22782324 (1998).
Article Google Scholar
Iandola, N. F., Han, S., Moskewicz, W. M., Ashraf, K., Dally, J. W. & Keutzer, K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv:1602.07360 (arXiv preprint) (2016).
Girshick, R., Donahue, J., Darrell, T. & Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition 580587 (2014).
Girshick, R. Fast R-CNN. In IEEE International Conference on Computer Vision (ICCV) 14401448 (2015).
Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 11371149 (2017).
Article PubMed Google Scholar
He, K., Gkioxari, G., Dollr, P. & Girshick, R. Mask R-CNN. In IEEE International Conference on Computer Vision (ICCV) 29802988 (2017).
Minaee, S. et al. Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 35233542 (2022).
PubMed Google Scholar
Zhao, Z. Q., Zheng, P., Xu, S. T. & Wu, X. Object detection with deep learning: A review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 32123232 (2019).
Article PubMed Google Scholar
Vinod, G. & Padmapriya, G. An adaptable real-time object detection for traffic surveillance using R-CNN over CNN with improved accuracy. In International Conference on Business Analytics for Technology and Security (ICBATS) 14 (2022).
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. You only look once: Unified, real-time object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 779788 (2016).
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S. Fu, Y. C. & Berg, A. C. Ssd: Single shot multibox detector. In European Conference on Computer Vision 2137 (2016).
Lin, T. Y., Goyal, P., Girshick, R., He, K. & Dollr, P. Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318327 (2020).
Article PubMed Google Scholar
Ma, Y., Yang, J., Li, Z., & Ma, Z. YOLO-cigarette: An effective YOLO network for outdoor smoking real-time object detection. In International Conference on Advanced Cloud and Big Data (CBD) 121126 (2022).
Redmon, J. & Farhadi, A. YOLO9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017).
Redmon, J. & Farhadi, A. Yolov3: An incremental improvement. In arXiv:1804.02767 (arXiv preprint) (2018).
Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. Yolov4: Optimal speed and accuracy of object detection. In arXiv:2004.10934 (arXiv preprint) (2020).
Zhu, X., Lyu, S., Wang, X. & Zhao, Q. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 27782788 (2021).
Wang, C. Y., Bochkovskiy, A. & Liao, H. Y. M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv:2207.02696 (arXiv preprint) (2022).
Qiu, M., Huang, L. & Tang, B. H. ASFF-YOLOv5: Multielement detection method for road traffic in UAV images based on multiscale feature fusion. Remote Sens. 14(14), 3498 (2022).
Article ADS Google Scholar
Sudars, K., Namatvs, I., Judvaitis, J., Balas, R., ikuins, A., Peter, A., Strautia, S., Kaufmane, E. & Kalnia, I. YOLOv5 deep neural network for quince and raspberry detection on RGB images. In Workshop on Microwave Theory and Techniques in Wireless Communications (MTTW) 1922 (2022).
Liu, W., Quijano, K. & Crawford, M. M. YOLOv5-tassel: Detecting tassels in RGB UAV imagery with improved YOLOv5 based on transfer learning. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 15, 80858094 (2022).
Article ADS Google Scholar
Originally posted here:
Improved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV ... - Nature.com
Bullbit Review: CFD-Based Design of Cryptocurrency Mining Facilities for Enhanced Heat Dissipation – Startup.info
Introduction
In the world of cryptocurrency mining, efficiency is key. Mining operations generate immense amounts of heat, which can lead to a variety of challenges, including equipment malfunction and increased energy costs. However, with the advent of CFD-based design solutions, such as those offered by Bullbit, mining facilities can now optimize their heat dissipation processes and improve overall operational efficiency. In this review, we will explore how Bullbit leverages computational fluid dynamics (CFD) to revolutionize cryptocurrency mining facility design.
Bullbit understands the unique challenges faced by cryptocurrency mining facilities, and their innovative approach to facility design sets them apart from other brokers in the industry. By employing CFD simulations, Bullbit is able to accurately predict and analyze airflow patterns, temperature distribution, and heat dissipation within mining facilities. This comprehensive understanding enables them to design efficient ventilation systems and optimize the placement of cooling equipment, resulting in improved heat dissipation and a more stable mining environment.
Bullbit recognizes that each mining facility is unique, with its own set of constraints and requirements. Thats why they prioritize customized facility designs to maximize mining performance. Through CFD simulations, Bullbit assesses factors such as room size, equipment layout, and environmental conditions, allowing them to create tailored solutions that align with the specific needs of their clients. By optimizing these factors, Bullbit ensures that mining operations can run at their full potential while minimizing operational costs.
One of the key aspects of Bullbits CFD-based design approach is the optimization of ventilation systems. Proper airflow is essential for dissipating the heat generated by mining equipment, and Bullbit utilizes CFD simulations to precisely analyze air movement patterns within the facility. By strategically positioning fans, ducts, and vents, they can effectively manage heat buildup, ensuring a more uniform temperature distribution throughout the facility. This results in a cooler environment for the mining hardware and reduces the likelihood of overheating or equipment failure.
Efficiency is not only about heat dissipation; it also encompasses energy consumption. Bullbit understands the importance of minimizing energy costs for mining operations. Through CFD simulations, they can identify energy-intensive areas within the facility and suggest improvements to optimize energy usage. By strategically locating cooling equipment, Bullbit ensures that energy is directed where it is needed most, reducing overall energy consumption and lowering operational costs for their clients.
Safety is a critical consideration in any mining facility, and Bullbit places great emphasis on ensuring the safety and compliance of their designs. By utilizing CFD simulations, they can identify potential hazards, such as hotspots or airflow obstructions, and mitigate them before construction begins. This proactive approach not only enhances the safety of the facility but also helps mining operators comply with relevant regulations and guidelines.
Bullbits CFD-based design solutions are built on a foundation of data-driven decision making. By collecting and analyzing vast amounts of data, they gain valuable insights into the intricacies of heat dissipation and airflow management. This data-driven approach allows Bullbit to make informed design choices, optimize cooling systems, and ultimately improve mining facility performance. Through their partnership with clients, Bullbit ensures that data is continuously monitored and used to drive ongoing facility improvements.
In addition to their focus on efficiency and performance, Bullbit also recognizes the importance of sustainability and environmental responsibility in the cryptocurrency mining industry. With CFD-based design solutions, they can implement strategies that promote energy efficiency and reduce the environmental impact of mining operations. By optimizing ventilation systems and cooling equipment, Bullbit helps mining facilities minimize their carbon footprint and contribute to a more sustainable future.
The commitment of Bullbit extends beyond the design phase. They provide continuous support and monitoring to ensure that the implemented solutions are functioning optimally. Through real-time data monitoring and analysis, Bullbit can identify any potential issues or areas for improvement promptly. This proactive approach allows them to work closely with their clients and address any concerns, further enhancing the efficiency and performance of mining facilities over time.
With years of experience in the field of cryptocurrency mining facility design, Bullbit has established a strong reputation for their expertise and professionalism. Their team of skilled engineers and designers possess a deep understanding of the unique challenges faced by mining operations. By staying up-to-date with the latest industry trends and technological advancements, Bullbit consistently delivers innovative and effective solutions that meet the evolving needs of their clients.
The satisfaction of Bullbits clients speaks volumes about the quality of their services. Numerous testimonials from mining facility operators attest to the positive impact of Bullbits CFD-based design solutions on their operations. Clients praise the improved heat dissipation, energy efficiency, and overall performance of their facilities after implementing Bullbits customized designs. The reliability and dedication of Bullbit have earned them a loyal client base and positive word-of-mouth recommendations within the cryptocurrency mining community.
Bullbit is at the forefront of CFD-based design solutions for cryptocurrency mining facilities. By leveraging computational fluid dynamics simulations, they optimize heat dissipation, improve energy efficiency, enhance safety, and maximize mining performance. Their commitment to customized solutions and data-driven decision-making sets them apart as trusted broker in the industry. If youre looking to enhance the heat dissipation capabilities of your cryptocurrency mining facility, Bullbit is undoubtedly a top choice that combines innovation, expertise, and a focus on delivering exceptional results.
Disclaimer: This is sponsored marketing content.
See the article here:
Colocation Data Center Market Projections Highlighting Primary … – The Bowman Extra
New Jersey, United StatesThe GlobalColocation Data CenterMarket is expected to grow with a CAGR of %, during the forecast period 2023-2030, the market growth is supported by various growth factors and major market determinants. The market research report is compiled by MRI by conducting a rigorous market study and includes the analysis of the market based on segmenting geography and market segmentation.
Moreover, the rising awareness about the benefits of Colocation Data Center, including improved efficiency, cost savings, and sustainability, is fostering market growth. Businesses across different sectors are recognizing the value of Colocation Data Center in streamlining operations, reducing environmental impact, and enhancing overall productivity.
Download a PDF Sample of this report: https://www.marketresearchintellect.com/download-sample/?rid=277606
The market study was done on the basis of:
Region Segmentation
Product Type Segmentation
Application Segmentation
MRI compiled the market research report titled GlobalColocation Data CenterMarket by adopting various economic tools such as:
Company Profiling
Request for a discount on this market study: https://www.marketresearchintellect.com/ask-for-discount/?rid=277606
To conduct a market study in-depth, MRI adopted various market research tools and followed a traditional research methodology is one of them, data and other qualitative parameters were analyzed by adopting primary and secondary research methodologies, which were explained in detail, as follows:
Primary Research
In the primary research process, information was collected on a primary basis by:
Basic information details were collected to collect quantitative and qualitative data, based on different market parameters, the data was organized and analyzed from both the demand and supply sides of the market.
Secondary Research
For secondary research, various authentic web sources and research papers/white papers were considered to identify and collect information and market trends. The data collected from secondary sources help to calculate the pricing models, and business models of various companies along with current trends, market sizing, and company initiatives. Along with these open-available sources, the company also collects information from various paid databases that are extensive in terms of information in both qualitative and quantitative manner.
Research by other methods:
MRI follows other research methodologies along with traditional methods to compile the 360-degree research study that is majorly customer-focused and involves a major company contribution to the research team. The client-specific research provides the market sizing forecast and analyzed the market strategies that are focused on client-specific requirements to analyze the market trends, and forecasted market developments. The companys estimation methodology leverages the data triangulation model that covers the major market dynamics and all supporting pillars. The detailed description of the research process includes data mining is an extensive step of research methodology. It helps to obtain the information through reliable sources. The data mining stage includes both primary and secondary information sources.
The report Includes the Following Questions:
About Us: Market Research IntellectMarket Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.
Contact Us: Mr. Edwyne FernandesMarket Research IntellectNew Jersey (USA)US: +1 (650)-781-4080 USToll-Free: +1 (800)-782-1768Website: -https://www.marketresearchintellect.com/
Read more here:
Colocation Data Center Market Projections Highlighting Primary ... - The Bowman Extra
AlphaGPT is actively recruiting talents worldwide to drive data asset … – Digital Journal
PRESS RELEASE
Published June 12, 2023
AlphaGPT is a leading artificial intelligence quantification company.
Leading AI quant trading company, AlphaGPT, is disrupting the industry with its cutting-edge technology and innovative approach to providing efficient and stable quant trading solutions to global investors. With a team of top-notch AI experts and financial analysts, AlphaGPT offers one-click quant trading strategies driven by advanced algorithms to optimize trading efficiency and accuracy.
AlphaGPT's core values of freedom and fairness are at the heart of its mission. The company believes in providing equal opportunities for all to participate in trading, realize value, and increase wealth. Through the use of Alpha robots, AlphaGPT enables intelligent quantification, a groundbreaking trading method that significantly enhances profitability and market understanding.
Alpha robots, equipped with state-of-the-art algorithms, employ data mining and deep learning techniques to analyze market trends, identify profitable trades, and execute them automatically. This advanced technology empowers investors to navigate market changes more effectively and maximize profits. AlphaGPT's global team of part-time employees is crucial in continuously improving the robots' quantification and mining capabilities through data input and learning.
AlphaGPT is embarking on a worldwide recruitment drive for part-time employees to expand its operations further. These employees will activate and operate the company's quant robots, contributing to the robots' development by providing essential data for analysis and learning. No specific major or experience is required, only a passion for learning, a serious work ethic, and recognition of AlphaGPT's mission. Part-time employees enjoy flexible work locations, including the opportunity to work from home or while on the go, a generous salary package, and substantial bonuses and benefits.
Beyond its quant trading solutions and recruitment efforts, AlphaGPT offers a comprehensive suite of services. Its parent company, Alpha Asset LTD, provides clients with quant trading strategy consulting, trade execution, risk control services, and portfolio management solutions encompassing asset allocation, risk management, and performance evaluation.
"AlphaGPT is at the forefront of revolutionizing the quant trading landscape," said the spokesperson for AlphaGPT. "Our commitment to utilizing cutting-edge AI technology, providing free and fair trading solutions, and offering flexible work opportunities to part-time employees sets us apart. If anyone is seeking a meaningful job with an innovative and forward-thinking company, AlphaGPT is an opportunity they cannot afford to miss.
For more information on AlphaGPT and its groundbreaking quant trading solutions, please visithttps://alphagpt.org.
Media ContactCompany Name: AlphaGPTContact Person: Marylou Santos Email: Send EmailCity: Manchester Country: United KingdomWebsite: alphagpt.org
Link:
AlphaGPT is actively recruiting talents worldwide to drive data asset ... - Digital Journal
Reference Management Tools Market 2023 Qualitative Insights, Key … – The Bowman Extra
New Jersey, United StatesThe GlobalReference Management ToolsMarket is expected to grow with a CAGR of %, during the forecast period 2023-2030, the market growth is supported by various growth factors and major market determinants. The market research report is compiled by MRI by conducting a rigorous market study and includes the analysis of the market based on segmenting geography and market segmentation.
Moreover, the rising awareness about the benefits of Reference Management Tools, including improved efficiency, cost savings, and sustainability, is fostering market growth. Businesses across different sectors are recognizing the value of Reference Management Tools in streamlining operations, reducing environmental impact, and enhancing overall productivity.
Download a PDF Sample of this report: https://www.marketresearchintellect.com/download-sample/?rid=274682
The market study was done on the basis of:
Region Segmentation
Product Type Segmentation
Application Segmentation
MRI compiled the market research report titled GlobalReference Management ToolsMarket by adopting various economic tools such as:
Company Profiling
Request for a discount on this market study: https://www.marketresearchintellect.com/ask-for-discount/?rid=274682
To conduct a market study in-depth, MRI adopted various market research tools and followed a traditional research methodology is one of them, data and other qualitative parameters were analyzed by adopting primary and secondary research methodologies, which were explained in detail, as follows:
Primary Research
In the primary research process, information was collected on a primary basis by:
Basic information details were collected to collect quantitative and qualitative data, based on different market parameters, the data was organized and analyzed from both the demand and supply sides of the market.
Secondary Research
For secondary research, various authentic web sources and research papers/white papers were considered to identify and collect information and market trends. The data collected from secondary sources help to calculate the pricing models, and business models of various companies along with current trends, market sizing, and company initiatives. Along with these open-available sources, the company also collects information from various paid databases that are extensive in terms of information in both qualitative and quantitative manner.
Research by other methods:
MRI follows other research methodologies along with traditional methods to compile the 360-degree research study that is majorly customer-focused and involves a major company contribution to the research team. The client-specific research provides the market sizing forecast and analyzed the market strategies that are focused on client-specific requirements to analyze the market trends, and forecasted market developments. The companys estimation methodology leverages the data triangulation model that covers the major market dynamics and all supporting pillars. The detailed description of the research process includes data mining is an extensive step of research methodology. It helps to obtain the information through reliable sources. The data mining stage includes both primary and secondary information sources.
The report Includes the Following Questions:
About Us: Market Research IntellectMarket Research Intellect provides syndicated and customized research reports to clients from various industries and organizations with the aim of delivering functional expertise. We provide reports for all industries including Energy, Technology, Manufacturing and Construction, Chemicals and Materials, Food and Beverage, and more. These reports deliver an in-depth study of the market with industry analysis, the market value for regions and countries, and trends that are pertinent to the industry.
Contact Us:Mr. Edwyne FernandesMarket Research IntellectNew Jersey (USA)US: +1 (650)-781-4080 USToll-Free: +1 (800)-782-1768Website: -https://www.marketresearchintellect.com/
Original post:
Reference Management Tools Market 2023 Qualitative Insights, Key ... - The Bowman Extra