Author: THANGAVELU CHINNASAMY

FEBRUARY 23 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்

FEBRUARY 22 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்

FEBRUARY 21 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்

ஒரு நொடி பா ‘அண்டவியல்’
தொன்மை வரலாற்று வரையறை அண்டவியல்
பன்முக படிமலர்ச்சி நிலையறிவு.

நடும் நடவு நட்ட பயிர்
கூடும் நடவியல் கணக்கு வைப்பு
தேடும் நடல் உள்ள மாநிலம்
வாடும் பயிர் செழிப்பை நாடும்.

நாடும் வீடும் நகரில் நகரும்
பாடும் பாடலும் பாடுபொருளே பாடசாலை
நாடுவதே நாட்டின் குறிப்பிட்ட செயல்
காடும் மேடும் கவனிப்பதே ஆட்சிமுறை.

ஆட்சிமுறை மாட்சி மாண்பு உடைமை
காட்சி கருத்து கணிப்பு முறையீடு
மாட்சிமை
உட்படும் மக்களின் உடைமையே குடியரசு
ஆட்சியின் தேவை தேர்ந்தெடுத்து வைப்பதே.

வைப்பு முறை இலக்கு குறியீடு
இப்பகுதியோர் புரியவே மாநிலத்து கல்வி
ஒப்பித்து படித்தவை உள்ளத்தில் நிலைக்கும்
அப்படி படிப்பதே எக்காலமும் சென்றிடும்.

சென்றிடும் செயல் திறன் என்றும்
நன்மை தரும் வாழ்வு முறை
தொன்மை வரலாற்று வரையறை அண்டவியல்
பன்முக ஆற்றலில் படிமலர்ச்சி நிலையறியும்.

FEBRUARY 20 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்

FEBRUARY 19 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்

கணக்கியல் நுண்ணறிவு AI – ACCOUNTING INTELLIGENCE & FI : Financial Intelligence

FI: Financial Intelligence

கணக்கியல் நுண்ணறிவு

கணநேர மதிப்பு மிகு இயல்பு
இணங்கிய நிலைய கால அளவு
கணமும் இயங்கும் சுற்றும் சுழல்
நாணய மதிப்பீடு தொகுதி தரவு.

தரவுக் கூறும் வகை நிலை
வரவு செலவு கணக்கு வைப்பு பதிவு
தரவுத்தள பகுப்பு அளவறியும் கூறுபாடு
பரவும் நிலைப்பு ஆற்றல் இலக்கு.

இலக்கு குறியீடு கணக்கிருப்பு பேரளவு
இலக்கத்தின் கூற்று விளம்பும் படியாக்கம்
இலக்கில் வைக்கும் அறிக்கைத் தளம்
நிலவும் வல்லமை மேலாண்மை திறமை.

திறமை துணிவு மேன்மை அளவிடும்
திறந்தவெளி பசுமை இல்லச் சூழல்காற்று
சிறப்புறும் உருப்படிவ அமைப்பு நிலைத்தருக்கும்
திறமுறை ஈட்டும் பொருள் வருவாய்.

Abstracts:
FI Environmental monitoring systems can analyze large datasets from sensors to monitor environmental parameters like air quality, water quality, and biodiversity, helping identify potential environmental issues. The Accounting Reality reference is to include the information contained in it using further more in usable manners.
The sustainable development in utilizing artificial intelligence (AI) is within the accounting practices to promote environmentally and socially responsible for business operations.
It is primarily by improving the accuracy, efficiency and comprehensiveness of environmental impact assessment and reporting.
This article explores the AI driven models with accounting process enumeration with Machine Learning and Natural Language Processing with the abundant resource’s available along with Ecolonomy measurement Values.
Keywords: Monitor, Reality, Ecolonomy, Environmental Impact.
——–

Introduction:

The environmental impact increases by improving data analysis based on various sources such as energy consumption, waste management system, etc., and should be evaluated in reality in accordance to the periodic destruction of the Earth on the environmental impact.
The ‘goals’, in accordance with Acrostic way of expression in English language may be defined as for Accounting Artificial
intelligence is ‘G’enerally ‘O’btaining ‘A’ccounting ‘L’evel ‘S’ystem.
Automated reporting
Accounting intelligence, AI, is in the state of being machine learning in large-scale language modeling to automate the process of collecting and compiling standard data, reducing manual errors and improving the efficiency of reporting tables.
The Predictive analytics would provide by analyzing historical data, Accounting Intelligence in AI can predict potential environmental risks and opportunities, allowing companies to proactively implement sustainable development goal strategies.
Predictive Analytics By analyzing historical data, accounting intelligence in AI can predict potential environmental risks and opportunities, enabling organizations to proactively implement sustainable growth targeting strategies.
Supply chain transparency in managing account details.
AI can be used to a large extent to analyze supplier data and identify potential sustainability risks in the supply chain.
AI with supply chain management accounting intelligence promotes responsible sources of practicing at every stage of production at diversity levels.

The Stakeholder engagement with AI-powered dashboards can provide clear and accessible sustainability information to stakeholders, enhancing transparency and accountability.
Several examples of AI applications in sustainable accounting in
Carbon footprint calculation method.
Ecolonomy (collectively termed eco-economy) combined with AI impact on carbon footprint accounting can help in the process of quantifying the greenhouse gas (GHG) emissions of an individual’s or multiple companies’ products.
A management accountant of any company with artificial intelligence can input data, analyze large datasets to identify patterns and trends, and predict future financial values.
They can also detect anomalies that indicates fraudster insights to support better decisions making in the hierarchy application.
‘Values’ defines as ‘ Visionary Accounting Leverage Unique Environmental Situation’, in Acrostic Definition for Ecolonomy.
In The Digitalization era, Ecolonomy as per the view of Bert Kroese, The Term GDP is a poor measure of welfare. The GDP Values focuses on the present value and ignores the future. The depletion sources of Production capacities does not portrait the value future prosperity.
The Tectonic Shifts in the Global Ecolonomy need to shift its information on Treating Data as Produced asset and its
Fintech Related environmental impact assessment and allocation of resources can provide adequate protection consumption values, compiling Ecolonomy activity with the system of national accounts.

Environmental Assessment:
The AI concept for strengthening of environmental accounting and sustainable finance on measuring and transforming investment decisions has been in the processing taken into consideration with accurate for an organisation striving for effective climate action.
Accounting for globalization understanding with Special Purpose Enterprises (SPE) can be calculated by sensors in factories, satellites producing images of forests.
Based on the company’s development designs, the company’s emissions process profile with Multinational companies can be compared and analyzed within a national accounting context.
A sustainable ecosystem of accounting for well-being at diversity levels is within the process of the National Accounting System.
Food Growth along with distribution of House Hold Amenities can be measured and using the Present AI driven models.
In the process of environmental degradation, the production cost frontier level does not depict the correct information of pollution in the process of the air atmosphere.
Natural capital as a distinct category of any enterprise has a large direction of atmospheric pressure and pollution levels in the destruction of earthly living standards.
The common shareable resources of degraded soil atmospheres are to promote the common shareable attributes of national accountability. Thus, a global competence center can focus on the development and use of natural resource assessment with Environmental Accounting Reality.

The Common public shareable resources of ‘Ecolonomy’ can be measured by calculating plus and minus Values in Genuine Progress Indicator (GPI) in Graded Existence.
These are the best way in which to measure the climate impact of an entity’s activities.
The Consumption data can accurately value and calculate a company’s carbon footprint by analyzing energy level with AI model. AI can analyze waste management system on the disposal patterns to identify areas for improvement and reduce waste generation.
The Water level usage monitoring apparatus with
AI-enabled sensors can monitor water consumption in real-time, allowing for efficient water management.
The accuracy of AI-driven with abundant challenges and considerations can provide with the sustainability reporting depends heavily on the quality of data collected.
AI’s algorithms should be designed to avoid bias and promote responsible decision-making with good ethical standards.
The Accuracy of an organizations environmental, social governance especially for Sustainability report should involve on the current trends with historical data reporting.
The Implementation costs for AI systems to report on ever increasing temperature is required to picturize in at every moment in the Industrial sectors so as to alert the significant improvements of Climate Changes and along with the Common shareable cost of Shareable resources futuristic upfront cost of investment decisions in general. For AI model, attention has to carry forward to make the deployment of AI Ethical standards with responsibility accounting.
‘AI holds significant promise for transforming sustainability accounting by improving environmental impact assessment and reporting.’
Through advanced data analytics, real-time monitoring and improved transparency, AI can help organizations achieve their sustainability goals, ensuring a more sustainable future.
Predictive modeling:
AI can build complex models to predict future environmental changes based on current data, enabling proactive measures to mitigate negative impacts.
Optimization algorithms:
AI can optimize resource allocation and energy usage in various systems, like smart grids and industrial processes, leading to reduced environmental footprint.
A life cycle assessment method can be used to assess the overall environmental impact of an artificial intelligence system, considering its development, operation and disposal phases.
Artificial Intelligence can contribute to ecological values in different types.
AI-powered image of Wildlife conservation recognition systems can monitor animal populations and identify threats to endangered species.
AI can analyze large climate datasets to predict future weather patterns and potential climate change impacts with Climate change modelling.
Agricultural operations should have sufficient impact on artificial intelligence levels.
It improves irrigation and fertilizer use in agriculture, reducing water wastage and environmental impact.
AI can improve waste sorting and recycling efficiency by identifying different waste materials.

Important considerations when measuring ecological values:
Data quality with quantification should have
Accurate and reliable environmental data which is crucial for effective AI analysis.
Ethical implications
AI systems must be developed and used responsibly to avoid unintended negative environmental consequences.
The Transparency involves in Account figures conclusions and explainability with the facts is to be understandable with the relevant AI model reaches its conclusions is important for evaluating its environmental impact.
Conclusion:
The Common public development in the surroundings is a permanent feature for encouraging people to understand and using in unity in diversity with Accounting in Reality.
There are a few key points for quantifying environmental values in AI
An environmental monitoring system problem should be readily accounted for. Transparency includes the results of accounting statistics and can be interpreted with facts, which is important to assess its environment when reaching its conclusions with the relevant AI model.
Reference: Information about Ecolonomy and Econology in ACROSTIC
Way in the below you tube video link.
(https://youtu.be/CbLLKevHaVI?si=jyALNvIDUgGe7xrz

FEBRUARY 18 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்

FEBRUARY 17 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்

FEBRUARY 16 – அகத்தவம் – ஆண்டின் ஒவ்வொரு நாளும்