Question Paper Solutions of chapter Validation strategies of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Retrofitting is an essential element of any comprehensive strategy for improving residential energy efficiency. The residential retrofit market is still developing, and program managers must develop innovative strategies to increase uptake and promote economies of scale. Residential retrofitting remains a challenging proposition to sell to homeowners, because awareness levels are low and financial incentives are lacking.

The U.S. Department of Energy's Building America research team, Alliance for Residential Building Innovation (ARBI), implemented a project to increase residential retrofits in Davis, California. The project used a neighborhood-focused strategy for implementation and a low-cost retrofit program that focused on upgraded attic insulation and duct sealing. ARBI worked with a community partner, the not-for-profit Cool Davis Initiative, as well as selected area contractors to implement a strategy that sought to capitalize on the strong local expertise of partners and the unique aspects of the Davis, California, community. Working with community partners also allowed ARBI to collect and analyze data about effective messaging tactics for community-based retrofit programs.

ARBI expected this project, called Retrofit Your Attic, to achieve higher uptake than other retrofit projects, because it emphasized a low-cost, one-measure retrofit program. However, this was not the case. The program used a strategy that focused on attics-including air sealing, duct sealing, and attic insulation-as a low-cost entry for homeowners to complete home retrofits. The price was kept below $4,000 after incentives; both contractors in the program offered the same price. The program completed only five retrofits. Interestingly, none of those homeowners used the one-measure strategy. All five homeowners were concerned about cost, comfort, and energy savings and included additional measures in their retrofits. The low-cost, one-measure strategy did not increase the uptake among homeowners, even in a well-educated, affluent community such as Davis.

This project has two primary components. One is to complete attic retrofits on a community scale in the hot-dry climate on Davis, CA. Sufficient data will be collected on these projects to include them in the BAFDR. Additionally, ARBI is working with contractors to obtain building and utility data from a large set of retrofit projects in CA (hot-dry). These projects are to be uploaded into the BAFDR.

In a large network of computers, wireless sensors, or mobile devices, each of the components (hence, peers) has some data about the global status of the system. Many of the functions of the system, such as routing decisions, search strategies, data cleansing, and the assignment of mutual trust, depend on the global status. Therefore, it is essential that the system be able to detect, and react to, changes in its global status. Computing global predicates in such systems is usually very costly. Mainly because of their scale, and in some cases (e.g., sensor networks) also because of the high cost of communication. The cost further increases when the data changes rapidly (due to state changes, node failure, etc.) and computation has to follow these changes. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient local algorithm which detect when the L2 norm of the average data surpasses a threshold. Then, we use this algorithm as a feedback loop for the monitoring of complex predicates on the data – such as the data’s k-means clustering. The efficiency of the L2 algorithm guarantees that so long as the clustering results represent the data (i.e., the data is stationary) few resources are required. When the data undergoes an epoch change – a change in the underlying distribution – and the model no longer represents it, the feedback loop indicates this and the model is rebuilt. Furthermore, the existence of a feedback loop allows using approximate and “best-effort ” methods for constructing the model; if an ill-fit model is built the feedback loop would indicate so, and the model would be rebuilt.

Performance evaluations of the test set using the training set.

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multi-stage machine learning (ML) approach to streamline the discovery and optimization of complex multi-metallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially a benchmark in electrocatalysts research.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

MS971: Data sharing agreement

The Enterprise Data Warehouse (EDW) market is experiencing robust growth, driven by the increasing need for businesses to consolidate and analyze large volumes of data for improved decision-making. The market, valued at $5075.2 million in 2025, is projected to exhibit significant expansion over the forecast period (2025-2033). While a precise CAGR is unavailable, considering the strong market drivers such as the rising adoption of cloud-based solutions, the growing demand for advanced analytics, and the increasing focus on data-driven strategies across various industries, a conservative estimate of the Compound Annual Growth Rate (CAGR) would fall within the range of 10-15% for the forecast period. This growth is fueled by the transition to cloud-based EDW solutions, offering scalability, cost-effectiveness, and enhanced accessibility compared to on-premise systems. Furthermore, the rising adoption of advanced analytics techniques like machine learning and artificial intelligence is further driving the demand for robust EDW solutions capable of handling and processing massive datasets effectively. The market segmentation reveals a strong preference for web-based solutions and a significant demand across applications like information processing, data mining, and analytical processing. Leading players like Amazon Web Services (AWS), Microsoft, and Snowflake are at the forefront of innovation, constantly introducing new features and capabilities to enhance the functionalities and user experience of their EDW offerings. The geographical distribution of the market shows substantial growth across North America and Europe, driven by higher technology adoption rates and increased investments in digital transformation initiatives. However, Asia-Pacific is anticipated to emerge as a rapidly growing region in the coming years, fueled by rising digitalization and the expanding adoption of EDW solutions among large enterprises and government organizations. The key restraints to market growth include the high initial investment costs associated with implementing EDW systems, the need for specialized skills and expertise for effective management and utilization, and concerns about data security and privacy. However, these challenges are progressively being addressed through the emergence of cost-effective cloud-based solutions and the development of user-friendly interface solutions. The market is expected to witness further consolidation as leading vendors continue to expand their product portfolios and service offerings to cater to the ever-evolving needs of the enterprises.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

According to Cognitive Market Research, the global Lifescience Data Mining And Visualization market size is USD 5815.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.60% from 2023 to 2030.

North America held the major market of more than 40% of the global revenue with a market size of USD 2326.08 million in 2023 and will grow at a compound annual growth rate (CAGR) of 7.8% from 2023 to 2030
Europe held the major market of more than 40% of the global revenue with a market size of USD 1744.56 million in 2023 and will grow at a compound annual growth rate (CAGR) of 8.1% from 2023 to 2030. 
Asia Pacific held the fastest growing market of more than 23% of the global revenue with a market size of USD 1337.50 million in 2023 and will grow at a compound annual growth rate (CAGR) of 11.6% from 2023 to 2030
Latin America market held of more than 5% of the global revenue with a market size of USD 290.76 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.0% from 2023 to 2030
Middle East and Africa market held of more than 2.00% of the global revenue with a market size of USD 116.30 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.3% from 2023 to 2030
The demand for Lifescience Data Mining And Visualizations is rising due to rapid growth in biological data and increasing emphasis on personalized medicine.
Demand for On-Demand remains higher in the Lifescience Data Mining And Visualization market.
The Pharmaceuticals category held the highest Lifescience Data Mining And Visualization market revenue share in 2023.

Advancements in Healthcare Informatics to Provide Viable Market Output

The Lifescience Data Mining and Visualization market are driven by continuous advancements in healthcare informatics. As the life sciences industry generates vast volumes of complex data, sophisticated data mining and visualization tools are increasingly crucial. Advancements in healthcare informatics, including electronic health records (EHRs), genomics, and clinical trial data, provide a wealth of information. Data mining and visualization technologies empower researchers and healthcare professionals to extract meaningful insights, aiding in personalized medicine, drug discovery, and treatment optimization.

August 2020: Johnson & Johnson and Regeneron Pharmaceuticals announced a strategic collaboration to develop and commercialize cancer immunotherapies.

(Source:investor.regeneron.com/news-releases/news-release-details/regeneron-and-cytomx-announce-strategic-research-collaboration)

Rising Focus on Precision Medicine Propel Market Growth

A key driver in the Lifescience Data Mining and Visualization market is the growing focus on precision medicine. As healthcare shifts towards personalized treatment strategies, there is an increasing need to analyze diverse datasets, including genetic, clinical, and lifestyle information. Data mining and visualization tools facilitate the identification of patterns and correlations within this multidimensional data, enabling the development of tailored treatment approaches. The emphasis on precision medicine, driven by advancements in genomics and molecular profiling, positions data mining and visualization as essential components in deciphering the intricate relationships between biological factors and individual health, thereby fostering innovation in life science research and healthcare practices.

In June 2022, SAS Institute Inc. (US) entered into an agreement with Gunvatta (US) to expedite clinical trials and FDA reporting through the SAS Life Science Analytics Framework on Azure.

(Source:www.prnewswire.com/news-releases/clinical-research-and-drug-development-accelerated-via-analytics-301571580.html)

Market Restraints of the LifeScience Data Mining And Visualization

Data Privacy and Security Concerns to Restrict Market Growth

Data privacy and security concerns emerge as key restraints in the Lifescience Data Mining and Visualization market. With the abundance of sensitive patient data involved in life sciences, maintaining robust privacy measures is critical. Stringent regulations, such as HIPAA and GDPR, require secure handling of healthcare data, contributing to operational challenges for data mining and visualization. Striking a balance between extracting valuable insights and safeguarding patient privacy becomes complex, slowing down the adoption o...

Average costs of outpatient services.

Association rules for LFLU → HFLU.

Web multi omics knowledgebase based upon public, manually curated transcriptomic and cistromic datasets involving genetic and small molecule manipulations of cellular receptors, enzymes and transcription factors. Integrated omics knowledgebase for mammalian cellular signaling pathways. Web browser interface was designed to accommodate numerous routine data mining strategies. Datasets are biocurated versions of publically archived datasets and are formatted according to recommendations of the FORCE11 Joint Declaration on Data Citation Principles73, and are made available under Creative Commons CC 3.0 BY license. Original datasets are available.

Number of rules for sample database 1.

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Mining Automation Market Size 2024-2028

The mining automation market size is forecast to increase by USD 1.87 billion at a CAGR of 7.92% between 2023 and 2028.

The market is experiencing significant growth due to the expansion of the mining industry and the increasing adoption of mobile-based technologies. The mining sector's growth is driven by factors such as increasing demand for minerals and metals, rising investment in infrastructure, and advancements in mining techniques. In addition, the use of mobile-based technologies, including autonomous vehicles and drones, is becoming increasingly popular in mining operations to improve efficiency and productivity.
However, the market also faces challenges, particularly in the area of cybersecurity. With the increasing use of automation and digital technologies in mining, there is a growing risk of cyber attacks, which could result in significant financial and operational losses. Therefore, mining companies must prioritize cybersecurity measures to protect their assets and maintain the trust of their stakeholders. Overall, the market is expected to continue growing, driven by these trends and challenges.

What will be the Size of the Mining Automation Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of advanced technologies such as remote operations, mine planning software, predictive maintenance, data management, and digital mine transformation. These innovations enable increased safety in open pit and underground mining operations, reducing hazardous environments for workers. Robotics and autonomous equipment are key components of this trend, driving efficiency, cost reduction, and optimization of production levels. Sustainability is a critical focus area, with mining companies investing in sustainable practices, safety regulations, and workforce development. Mine safety training and governance are essential for ensuring compliance with evolving legislation.
Data analytics and digital mine transformation are essential for improving business strategies, enhancing mine site security, and minimizing environmental impact. Investment opportunities In the mining automation industry are abundant, with ongoing research and development leading to continuous innovation. The economic impact of these advancements is significant, as mining companies seek to stay competitive in a rapidly changing market. Overall, the market is poised for continued growth, with a strong emphasis on safety, optimization, and sustainability.

How is this Mining Automation Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Equipment
  Software
  Communication system


Type

  Underground mining automation
  Surface mining automation


Geography

  APAC

    China
    Japan


  North America

    US


  Europe

    Germany


  South America



  Middle East and Africa

By Component Insights

The equipment segment is estimated to witness significant growth during the forecast period. The market encompasses the use of advanced technologies, including artificial intelligence (AI), robotization, wireless sensors, RFID, data communication, and visualization tools, to automate mining operations. This market caters to various mining activities, such as base metals exploration and extraction, drilling in oil sands and underground mines, and waste management. Automated solutions employ autonomous technology to operate equipment, including trucks, drillers, and loaders, in real-time, enhancing production efficiency and safety. Safety integrity level is a crucial aspect, ensuring the safety of workers in hazardous conditions. Hardware automation technology, such as wireless networks and asset management strategies, streamlines operations and minimizes human error.

Mining automation technologies also facilitate predictive maintenance and resource extraction through the integration of IoT and data analytics. Key mining sectors include coal, metals, and mineral processing, with applications in drilling, material handling, and materials processing. Safety standards are paramount, addressing equipment failures and hazardous working conditions.

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The equipment segment was valued at USD 1.27 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 42% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forec

A KNOWLEDGE DISCOVERY STRATEGY FOR RELATING SEA SURFACE TEMPERATURES TO FREQUENCIES OF TROPICAL STORMS AND GENERATING PREDICTIONS OF HURRICANES UNDER 21ST-CENTURY GLOBAL WARMING SCENARIOS

CAITLIN RACE*, MICHAEL STEINBACH*, AUROOP GANGULY**, FRED SEMAZZI***, AND VIPIN KUMAR*

Abstract. The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurricanes or tropical cyclones are among the least understood in climate science but among the most fiercely debated in the context of adaptation decisions or mitigation policies. Here we show that a knowledge discovery strategy, which leverages observations and climate model simulations, offers the promise of developing credible projections of tropical cyclones based on sea surface temperatures (SST) in a warming environment. While this study motivates the development of new methodologies in statistics and data mining, the ability to solve challenging climate science problems with innovative combinations of traditional and state-of-the-art methods is demonstrated. Here we develop new insights, albeit in a proof-of-concept sense, on the relationship between sea surface temperatures and hurricane frequencies, and generate the most likely projections with uncertainty bounds for storm counts in the 21st-century warming environment based in turn on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Our preliminary insights point to the benefits that can be achieved for climate science and impacts analysis, as well as adaptation and mitigation policies, by a solution strategy that remains tailored to the climate domain and complements physics-based climate model simulations with a combination of existing and new computational and data science approaches.

Abstract:

Release is a ubiquitous concept in software development, referring to grouping multiple independent changes into a deliverable piece of software. Mining releases can help developers understand the software evolution at coarse grain, identify which features were delivered or bugs were fixed, and pinpoint who contributed on a given release. A typical initial step of release mining consists of identifying which commits compose a given release. We could find two main strategies used in the literature to perform this task: time-based and range-based. Some release mining works recognize that those strategies are subject to misclassifications but do not quantify the impact of such a threat. This paper analyzed 13,419 releases and 1,414,997 commits from 100 relevant open source projects hosted at GitHub to assess both strategies in terms of precision and recall. We observed that, in general, the range-based strategy has superior results than the time-based strategy. Nevertheless, even when the range-based strategy is in place, some releases still show misclassifications. Thus, our paper also discusses some situations in which each strategy degrades, potentially leading to bias on the mining results if not adequately known and avoided.

Instructions:

Visit https://github.com/gems-uff/release-mining for instructions about how to use this dataset.

Files:

The repos.tgz contains our project corpus comprising 1,414,997 releases from 100 relevant open source projects.

The repos.sha1 contains the sha1 checksum of repos.tgz

Disclaimer:

This replication package contains the source code of 100 relevant open source projects. Its purpose is to enable the replication of the study conducted in the paper "Assessing time-based and range-based strategies for commit assignment to releases."

It is essential to check each project license before using the source code or any attached file for any other purposes besides replicating the study.

Background: Biomechanical studies of ACL injury risk factors frequently analyze only a fraction of the relevant data, and typically not in accordance with the injury mechanism. Extracting a peak value within a time series of relevance to ACL injuries is challenging due to differences in the relative timing and size of the peak value of interest.

Aims/hypotheses: The aim was to cluster analyze the knee valgus moment time series curve shape in the early stance phase. We hypothesized that 1a) There would be few discrete curve shapes, 1b) there would be a shape reflecting an early peak of the knee valgus moment, 2a) youth athletes of both sexes would show similar frequencies of early peaks, 2b) adolescent girls would have greater early peak frequencies.

Methods: N = 213 (39% boys) youth soccer and team handball athletes (phase 1) and N = 35 (45% boys) with 5 year follow-up data (phase 2) were recorded performing a change of direction task with 3D motion analysis and a force plate. The time series of the first 30% of stance phase were cluster analyzed based on Euclidean distances in two steps; shape-based main clusters with a transformed time series, and magnitude based sub-clusters with body weight normalized time series. Group differences (sex, phase) in curve shape frequencies, and shape-magnitude frequencies were tested with chi-squared tests.

Results: Six discrete shape-clusters and 14 magnitude based sub-clusters were formed. Phase 1 boys had greater frequency of early peaks than phase 1 girls (38% vs 25% respectively, P <  0.001 for full test). Phase 2 girls had greater frequency of early peaks than phase 2 boys (42% vs 21% respectively, P <  0.001 for full test).

Conclusions: Cluster analysis can reveal different patterns of curve shapes in biomechanical data, which likely reflect different movement strategies. The early peak shape is relatable to the ACL injury mechanism as the timing of its peak moment is consistent with the timing of injury. Greater frequency of early peaks demonstrated by Phase 2 girls is consistent with their higher risk of ACL injury in sports.

This publication ‘Strategic Environmental Assessment – Guidelines for Pacific Island Countries and Territories’ has been prepared to provide guidance on the application of SEA as a tool to support environmental planning, policy and informed decision making. It provides background on the use and benefits of SEA as well as providing tips and guiding steps on the process, including case studies, toolkits and checklists for conducting an SEA in the Appendices.

These guidelines are intended to assist the national and local authorities such as Environment Agencies and National Planning Offices, development control agencies, municipal authorities, provincial administrations and Strategic Development Offices in Pacific Island Countries and Territories with an understanding of what Strategic Environmental Assessment is, the benefits that can be achieved through its targeted use, and how and when to apply it to ensure that environmental and social matters are integrated into policies, plans, programmes and projects. The guidelines can also be used by other government sectors in terms of developing and implementing new policies and programs for the government. These guidelines can also provide useful assistance to non-governmental organisations, communities and all those seeking to broaden their capacities, with a view of better informed public participation in strategic planning.