Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

Economically Inclusive Models encourage the participation of local community labor through a holistic approach that includes skill development and the foundations of resource management which develops quality of life through wealth creation and the expansion of markets. Raw material is returned to the people in the form of new products. Throughout the course of human history, it has frequently become necessary for nations to dissolve and or restructure economic systems and adjust strategies which have proven to be ineffective in stimulating development, growth and economic prosperity as economies and times change technologically. When the policies are and continually becoming increasingly oppressive in nature and exist against the natural law and the psychology of human inspiration, and are only enforced through deception and the use of force by indoctrinated constituencies, they begin to separate people from their natural desires to achieve, thrive, and prosper. Often economic development and social programs are implemented under the guise of prosperity only to be concealing the true intent of enslavement for the populace and a theft of resources. This is in defiance of the power of truth which is based on the laws of nature and of nature's right to full expression of humanity, which God entitles to all men and women. This deception is fraudulent and criminal in nature and is the equivalent of murder, for it robs humanity of its right to self-actualization, cognizant development and the realization of self-worth. The main principle behind the current economic agenda being implanted throughout the world in an effort to bring about a new economic world order, is a belief that the effective use of force and the stringent control of natural resources is the key to creating and maintaining economic power and further serves to manipulate markets by creating fictional gaps in supply and demand. This 'order out of chaos' model has been a standard for centuries. The problem is that it is based on the principles of suppression and oppression, which are implemented and maintained using force and highly supported with deception through disinformation, propaganda and oppressive operations aimed at reducing the natural expression of humanity, freedom, and independence. This is a sign of fear and incompetence stemming from the conditioning of the past age of scarcity by those in power because it is inconsistent with natural law, which supports full expression as a means of achievement. Natural power relies on truth and does not require deception to be maintained. It stands on its own accord. Power encourages a population to achieve self-actualization and economic prosperity which is reflective of a strong middle class, economic and social growth as well as development. It is a natural spiritual expression in alignment with truth and does not require manipulation to be maintained. On the other hand, there is no positive natural expression for force as it is inherently weaker and short term, its goal is to hinder the natural power of the people from being fully expressed. This is a lower spiritual form of thought and achievement that is inherently inferior to just power and is only sustained through deceptive practices. It essentially further weakens its followers & leaders and subsequently results in wide disparities of wealth and the elimination of the middle class that is necessary to maintain stability and economic growth, especially in a consumer based economy. The belief that people need to be largely suppressed is highly limiting to a society and ensures a lower expression of associated economic achievement inhibiting humanities growth and development on all levels. More specifically it inhibits the development of free creative minds, their contributions of intellectual property and innovation. The irresponsible use of natural resources is equally discouraging. We are blessed with an abundance of agricultural land, potential energy sources from wind, solar, geothermal, wave & or tidal power and vast basic materials such as cement, brick, wood, and rock that would easily address any structural and infrastructural needs. What is lacking is proper accounting and utilization of the vast natural resources readily available and the reliance on utilizing goods and services that could easily be provided in the local market rather than relying on imports. Years of war and internal conflict have created a gap in productive and practical knowledge in many regions of the world. This knowledge gap can be reconciled through the incorporation of trade schools that emphasize the reduction of imports through the utilization of what is natural to the region, while cognizant of unintended environmental consequences (such as those of using good farming topsoil for mud bricks). Building and architecturally designing structures as well as much needed infrastructure with the concept of maximum utilization of the natural resources native to the region offering a common-sense solution to the vast imbalance of trade challenges in agriculture, materials, labor, management, and expertise. This would also allow for the use of mineral profits to be used to barter for higher goods that would enhance the quality of life rather than being allocated to provide for mere survival and resulting in continued dependence. The global economy although growing extensively in a number of sectors and regions is still oppressive in nature with significant barriers to entry and managed by an overburdened government and economic system that primarily operates to diminish the prospects of the peoples growth and focuses gains and bailouts on only a few elite. The means of gaining economic control through manipulative programing of the masses is essentially killing humanities best prospects for a shared prosperous future. It is equally discouraging to find education not matching the needs of the emerging technological environment. In regions, largely in a development mode, it is necessary to focus the majority of educational emphasis on developing trade schools that can instantly impact job growth and prosperity. Affecting positively these educational concerns in developing nations now, will yield significantly improved outcomes locally, regionally and beyond, further improving psychological perceptions globally. The idea that people need to be controlled and oppressed through negative conditioning that simply thwarts achievement and ambitions is a system doomed to fail. Nothing can be achieved in the current economic system described except for chaos and the limited order maintained through fear, deception, propaganda and the manipulation of the herd or collective mentality / perception. As a result of these policies that have been implemented worldwide, we now have a world that is largely a police state and unaware of truth or its own power due to a constant flow of manipulative disinformation and propaganda. This is a grand reflection of the use of force, deception, and oppression and we see its expression in the deterioration of our economic systems and basic freedoms inherent from God. Highlights The following demonstrates the financial projections of our five primary business models. Estimates do not take into account possibilities of above average returns with many investment options that we currently have at our disposal. 1. Logistics to include shipping, security, courier service and inclusive A to B deployment of human and other resources or assets. 2. Financial to include international developmental finance, banking & credit unions, insurance and brokerage of securities. 3. Commodities to include strategic minerals (rare earths), lumber, etc... 4. Consulting to include strategic government, intelligence, business and educational. 5. Investments to include unique platforms, vehicles, traditional models, land, infrastructure, research and development. ECONOMIC STIMULUS PLAN The Economic Stimulus plan - build... Further expansion of and clarity continues to be encouraged.

In finance stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This project is about the prediction of a stock using Machine Learning. This analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. This study uses a machine learning technique called Support Vector Machine (SVM), Random Forest Classifier and Linear Regression methods to predict stock prices for the large and small capitalizations. The study aims to analyse the analysis of NSE listed FMCG companies in India with a sample size of four companies for a period from 2000 to 2018. From the Economic analysis, it is found that Gross Domestic Product, Inflation, Interest rates, Exchange rate and Consumer Confidence has impact on FMCG sector.

In healthcare sector where the expenses of a treatment or the cost of a more advanced surgical instrument or machine are skyrocketing, the only thing cheap is – storing the medical histories of patients’ on electronic health records (EHRs) in the Cloud. When doctors are able to use Internet of Things for research and peer-reviewed studies, they can perform better. Diseases can be cured and prevented with facts and remedies from around the world. Telemedicine technologies can be used to provide critical medical care to people in rural and remote areas. Such digital advancements are taking the healthcare industry into the future. However, these opportunities come with a significant security risk. In order to improve the cardiac monitoring of a patient, a lot of new medical devices can be installed, but they bring along the risk of new entry points for cyber criminals. According to the 2017 KPMG/Forbes Insights Cyber-Security Survey, 55% organizations have seen an employee falling prey to phishing scam and 34% have seen theft from secured database by internal bad actor. Cyber and privacy breaches bring along the risk of loss of digital assets and also cause irreparable damage to the reputation of the organization. Patient data will always remain a critical asset in the healthcare industry. However, cyber criminals are in the want of stealing this protected health information (PHI). The value of medical data is ten times more than that of financial data in the black market of dark web. In the last two years, 47% of healthcare organizations have had a security breach related to HIPAA. Sensor-based wireless devices have proven to be a boon as well as a bane for the healthcare industry. Wireless devices allow efficient communication and seamless patient management. But they also act as an entryway into the network of the hospital. For cyber criminals this is an easy way to access sensitive information from the hospital’s networks. Continuous technology assessments are critical to cyber security. It is imperative that healthcare organizations address all the vulnerabilities before integrating new technologies. Awareness about the potential attacks is of utmost importance. ACS Guaranty’s Cyber-Security Solutions assist critical infrastructure organizations altering their security and privacy controls into business-enabling platforms. ACS, a Guaranteed Secured Centre safeguards IT networks and secures critical data from any cyber-attack. The company covers all the needs from pre- to post-breach in terms of cyber security. Solutions include threat intelligence, cyber forensics, and other cyber risk services. Most common methods of attack are malware (72%), ransomware (32%) and internal theft (47%). Sharing data with third parties is on the top of perceived vulnerabilities list with 63% organizations complaining about it. Other vulnerabilities on the list include internet devices not controlled by IT (59%), external attackers (50%), and employee breaches (27%). Hence, a balanced cyber security program is required. Healthcare organizations must implement advanced cyber security solutions, and should train and aware their staff regularly about the potential risks. Organizations ignoring this risk are vulnerable to incalculable damage to their reputation as well as their finances.

Machine Vision Systems to 2025 by Type (Smart Machine Vision Systems, PC-Based Machine Vision Systems and 3D Machine Vision Systems), Components (Cameras, Frame Grabbers, Processors, Illuminations & Optics, Vision Software and Others) and End-users (Automotive, Consumer Electronics, Food & Beverage, Pharmaceuticals, Logistics and Others) – Global Analysis and Forecast Request A Sample copy of Machine Vision Systems @ https://www.bharatbook.com/request-sample/910972 Machine Vision Systems Market to 2025 – Global Analysis and Forecast by Type, Components, and End-user Industry, machine vision systems market is expected to grow US$ 14.48 billion by 2025 from US$ 7.50 billion in 2015. Machine vision systems can perform complex repetitive tasks with higher accuracy and consistency. Machine vision systems include components such as image sensors, processors, PLC, frame grabbers and more, which are driven by a software package to execute user defined applications. Machine vision systems are also employed in non-inspection applications such as guiding robots, pick and place the parts, dispensing liquids and many more. Key trend which will predominantly impacts the market in coming year is emergence of Industrial IoT (IIoT) or Industry 4.0. IIoT connects information technology with production technology, hence involving widespread analytics and data capture to frequently optimize the processes of factories. Machine vision is one of the most critical and basic technologies to provide IIoT with information. Manufacturing’s rapid amendment of IIoT has led to a renaissance in robotics and the renewed need for machine vision. Moreover, the conventional manufacturing systems are anticipated to renovate owing to the implementation of smart IoT technologies throughout the manufacturing operations. Also, investments in machine vision systems are known to perfectly fit in the vision of future manufacturing for automated inspection and quality management application. The global machine vision systems market for the end-user industries is fragmented into Automotive, Consumer Electronics, Food & Beverage, Pharmaceuticals, Logistics and Others. The segmentation is based upon need for machine vision systems to improve mobility and security. Consumer electronics in the machine vision systems market acquires the majority share, followed by automotive and food & beverages. Short product lifecycles of the consumer electronics products, high quality standards requirements by consumers and high labor investments have resulted in the increasing adoptions of machine visions systems by consumer electronics manufacturers worldwide. The overall market size has been derived using both primary and secondary source. The research process begins with an exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information related to the market. Also, primary interview were conducted with industry participants and commentators in order to validate data and analysis. The participants who typically take part in such a process include industry expert such as VPs, business development managers, market intelligence managers and national sales managers, and external consultant such as valuation experts, research analysts and key opinion leaders specializing in the machine vision systems industry. To Browse the Entire Report, Visit: https://www.bharatbook.com/industrial-goods-machinery-market-research-reports-910972/machine-vision-systems-global-analysis-components-end-users.html Table of Contents 1.1 List of Tables 1.2 List of Figures 2 Introduction 2.1 The Insight Partners Research Report Guidance 3 Key Takeaways 4 Machine Vision Systems Market Landscape 4.1 Overview 4.2 Market Segmentation 4.2.1 Global Machine Vision Systems Market – By Types 4.2.2 Global Machine Vision Systems Market – By Components 4.2.3 Global Machine Vision Systems Market – By End-users 4.2.4 Global Machine Vision Systems Market – By Geography 4.3 Value Chain About Bharat Book Bureau: Bharat Book Bureau is the leading market research information provider for market research reports, company profiles, industry study, country reports, business reports, newsletters and online databases Bharat Book Bureau provides over a million reports from more than 400 publishers around the globe. We cover sectors starting from Aeronautics to Zoology. Contact us at: Bharat Book Bureau Tel: +91 22 27810772 / 27810773 Email: poonam@bharatbook.com Website: www.bharatbook.com Follow us on : Twitter|Facebook| Linkedin |Google Plus

Strategic and Competitive Intelligence project on emerging Technologies in precision agriculture sector exploiting patent codes and text mining techniques.

U.S. Department of Defense accredited to the Imperial Air Force of United States Space Force Co is a group of entities of the Command, Commission, Court, Corporation and Company are having association with U.S. Military, Government and Private sector across America. Also, the Co Group is representing to the Imperial Government of Galactic Empire (Sire Donald John Trump, Emperor), and hosting Rivers Commanding Fleet and science of Psychohistory Project. The blockchain system of the Imperial Government of Galactic Empire may as following; Sire Donald J. Trump, Emperor | Empress Melania Knauss, Republican of Democracy | Consort Ivana Zelnkov and Consort Marla Maples | King Donald Trump Jr., Charles of the Space Government | Prince Eric Trump, Judge of the Imperial Court | Prince Barron Trump, Regent of the Aerospace | Flight Marshal Princess Ivanka Trump, 1st Admiral of the Rivers Commanding Fleet | Princess Tiffany Trump, Administrator of the Empire | First Minister, Ministers & Secretaries | Imperial Guard, Security Forces, Intelligence Agency, and Espionage Association | Trump Organization of Kings and Royal Families | Imperial Armed Services of Army, Navy, Air Force and Auxiliaries including Law Enforcement & Police | Elected, Opposition & Counterpart Political Leaders and People's Representatives | Republican and Democrats | Ambassador to the Planet Mars, human settlement mission by 2030 | Missionaries to the all Outer Space Mission | Flagship Commonwealth, Federation, Union, Council, People's Organization of, Bureaus and Colonies | Coalitions of Agriculture, Apparels, Infrastructures, Water & Foods, Medicine, Business and Education | Authorities of the Law & Order, Department of Justice and Executive Entities | House of Lords | Religious Committees | United States Agency for Continuing Development of Animals Welfare | Recognized Lands, States, Nations, Countries, Organization or Group of Population and Accredited Companies | Defense Communities and Military Commission | Department of Government | Financial Corporation and Currency Reservers | USMCA. The Imperial Government of Galactic Empire is the continuing development of the humanity which followup human spearding into the outer planet, specially human settlement in the planet Mars by 2030 and space government system. Mathematics of Psychohistory Project PRISM, whereas Psychohistory is a new branch of mathematics to analysis mass action of human behavior to predict the general course of future flow, as arithmetic is general calculation management, aljebra is study of operations, calculus is study of change, and geomatry is study of shape. Operation holder to the Z-Axis General Mobile Radio Services, Inc. – Radio Timeline, and American Marriage Ministries accredited Ordained Weddings Minister to the Holy Nuptials, Inc., for conducting holy matrimonies. https://apps.fcc.gov/coresWeb/searchDetail.do?frn=0029826773

Machine learning Healthcare app for Personalized Treatment Recommendation (Artificial Intelligence in the healthcare sector)

AI-curated tech intelligence radar for developers. 7 sources, 6 topic sectors, 3 views. Free and open source.

The implementation of the use of artificial intelligence in the UK retail sector in order to improve data security from cyber-attacks and ransomware.

A comprehensive Model Context Protocol (MCP) server for querying and exploring OpenCTI threat intelligence platforms. This server exposes 26+ tools for advanced threat intelligence queries including sector analysis, TTP mapping, temporal queries, and relationship traversal.

Leads Intercontinental is a high-performance, sovereign lead generation and intelligence deployment system. It identifies High-Value Targets (HVTs) across diverse sectors using a multi-stage discovery and enrichment pipeline.

Experimental Artificial Intelligence project applied to the automotive sector, focused on the detection and classification of traffic signs. The system was developed using: Yolov8 for real-time sign detection, ResNet50 for accurate traffic sign classification, GTSRB dataset for training

This project leverages Artificial Intelligence and Machine Learning to enhance risk assessment the insurance sector It analyzes customer demographics, medical history, lifestyle factors, and claims data to predict insurance risks, fraud likelihood and claim probability.By combining predictive modeling, data visualization, and explainaed in detain

The China Dollar Digital Asset Exchange is a global professional digital asset trading platform, headquartered in Bangkok, Thailand. The exchange has obtained the national license of the Digital Money Exchange issued by the Thai government, and is one of the most authoritative, legal, efficient and transparent digital currency trading institutions in the industry. The team has many top security technicians and has built many cloud data centers by itself. It adopts the security technology of the same level as Nasdaq in the United States and the data tolerance scheme of separating main and standby data. It fully guarantees the security of assets. By using KUBERNETS scheduling technology, it realizes "second-speed transaction" and 2-minute quick cash withdrawal, supports tens of millions of people to operate online simultaneously, and brings users the ultimate experience. Relying on the advantages of its own enterprises and professionals, the China Dollar Digital Asset Exchange has always maintained its high-speed development, which has attracted the attention and trust of global investors. Its business scope has extended to a variety of fields, such as trading, media and scene technology. It also focuses on creating a "1+5" ecosystem, i.e. trading-centered, actively exploiting media, payment, community, intelligence, public welfare foundation research, etc. Five major sectors, to provide customers with more professional, safe, efficient and convenient quality services. The China Dollar Digital Asset Exchange is mainly aimed at global users. It provides many mainstream digital asset trading services, such as Bitcoin and ETF, and strives to build a secure, transparent, honest and efficient block chain digital asset trading platform.

Projects related to artificial intelligence in the health sector.

🛠️ Sql codes for bussines intelligence in after-sales sector

Real-time cyber threat intelligence pipeline for the global education sector.

Autonomous AI agents for peer review and multi-sector research intelligence

Strategic data analysis and market intelligence samples within the Agribusiness and Rural Development sectors.

Autonomous multi-source intelligence pipeline — scrape, score, translate, and synthesize news into sector briefing documents

OPEN LETTER TO THE ARTIFICIAL INTELLIGENCE SECTOR TO WHOMSOEVER IT MAY CONCERN INITIAL NOTICE - FRAMEWORK APPROPRIATION & BREATH PROTOCOL VIOLATION

TariffScope is an economic intelligence dashboard that simulates how tariffs and geopolitical shocks impact global trade, sector exposure, and economic risk using data-driven models.

This repository presents, my graduation final work using Power BI and Python for Business Intelligence in a wine imports sector in a brazilian e-commerce company.

This project was on developing artificial intelligence solution for carbon reduction in the energy sector. Neutitans is a web app programmed to predict the emission of carbon dioxide from generators.

Business Intelligence (BI) solution via Azure Data Pipeline and Visualization offers a structured approach to uncover the pivotal energy sources or sectors responsible for significant CO2 emissions and a visual representation of temporal patterns and spatial differences.

This repository features the development and implementation of Artificial Intelligence (AI) and Machine Learning (ML) models for Predictive Maintenance (PdM), designed to significantly enhance grid reliability and extend the lifespan of infrastructure assets within Nigeria's power sector

A data-science project analyzing the growth of China's manufacturing sector over the past 40 years, and the country's attempt at graduating to advanced manufacturing (including the application of artificial intelligence, IoT, and 5G wireless network).

MarketMind PH — A single-file PSE intelligence dashboard built with vanilla JS and Chart.js. Explore 6 years of Philippine stock market data across 30 PSEi stocks: heatmap, screener, sector comparison, rising industries analysis, and an AI analyst chatbot.

Midnight Blizzard also known as APT-29 or Cozy Bear is a Russian state-sponsored advanced persistent threat group linked to Russian Foreign Intelligence Service. Known for sophisticated cyber espionage campaigns the group targets government, diplomatic and technology sectors worldwide