Major Subfields of AI - Robotics

 Robotics is a major subfield of AI that deals with designing, constructing, and operating robots to carry out different jobs without or with minimal human intervention. It integrates AI with engineering to develop smart machines that can perceive, think, and engage with their environment. It also includes computer vision, machine learning, and control systems.

Fundamental Elements of Robotics

• Creating the various mechanical components of a robot, from the design to the assembly of the actuators and the sensors, is fundamental to the construction and design of a robot.
• Robots apply various tools and techniques, such as sensors and computer vision, to perceive and understand the world around them, which is essential for navigation and recognizing objects.
• Robots must improve their performance using statistical learning. Autonomous decision-making constitutes a complex cognitive skill that can be learned through trial and error.
• Robots must be able to execute a sequence of tasks and control their movement and balance through software and algorithm control which allows them to automate control performance and execute a sequence of tasks.
• Human-robot systems can use natural language processes for voice command recognition and other techniques for interaction to provide intuitive control and collaboration of a robot with a human.
• Robots can be programmed to create paths that link two locations and to navigate complex environments through a process of selection.

Major Subfields of AI - Natural Language Processing (NLP)

 Natural Language Processing (NLP) is a subdiscipline of AI that aims at allowing computers to comprehend and manipulate human language. The most important ones are Natural Language Understanding (NLU) and Natural Language Generation (NLG), which processes and generates language respectively. The fundamental operations include text analysis by use of tasks such as tokenizing and part-of-speech tagging and the applications are broadly used in areas such as translation, sentiment analysis and chatbots.

Core componentsNatural Language Understanding (NLU): The process of enabling a computer to derive meaning from human language, often involving mapping input to a useful representation.
Natural Language Generation (NLG): The process of producing meaningful human-like text from data or an internal representation.

Common tasks and techniques

• Text Preprocessing:Tokenization: Breaking text into smaller units like words or sentences.
• Part-of-Speech (POS) Tagging: Assigning a grammatical category (e.g., noun, verb) to each word.
• Named Entity Recognition (NER): Identifying and categorizing entities such as people, organizations, and locations.
• Syntax and Parsing: Analyzing the grammatical structure of a sentence.
• Semantic Analysis: Understanding the meaning of words in a given context.
• Discourse and Pragmatic Analysis: Analyzing the relationships between sentences and understanding context.

Key applications

• Machine Translation: Translating text from one language to another.
• Sentiment Analysis: Determining the emotional tone or opinion expressed in text, often used for social media and customer feedback.
• Chatbots: Creating conversational interfaces that can understand and respond to user input.
• Text Summarization: Automatically creating a concise summary of a larger document.
• Information Extraction: Pulling specific, useful data from unstructured text.

Major Subfields of AI - Machine Learning (ML)

Machine learning (ML) represents an important sub-field of AI where algorithms are utilized to learn when presented with data, and its key sub-types are supervised, unsupervised, semi-supervised and reinforcement learning. Additional essential fields in or connected to the field of ML are deep learning, based on neural networks; self-supervised learning and transfer learning. 

Key subfields of ML

• Supervised Learning: This is where algorithms are trained on labelled data to make a prediction.
• Unsupervised Learning: It is a training process which learns to identify patterns and structures using data which is not labeled.
• Semi-Supervised Learning: Labelled and unlabelled data are combined to acquire an understanding.
• Reinforcement Learning: These algorithms learn on a trial and error basis being rewarded or punished by their actions.
• Deep Learning: A sub-field of ML based on deep neural networks inspired by the human brain to learn based on large datasets.
• Self-Supervised Learning: This is a form of supervised learning, except that the data is also self-labeled.
• Transfer Learning: The model that has been trained on one task is re-used as the initial point of a model on a second task. 

Applications of ML

• Image recognition and speech recognition.
• Predictive analytics
• Recommendation systems
• Natural language processing (NLP)?
• Computer vision 

Major Subfields of AI - Knowledge Engineering

 Knowledge engineering is not an important subfield of AI per se, but a process that facilitates other areas such as expert systems. It deals with acquisition, modeling, and management of knowledge required to construct intelligent systems, and as such, constitutes an important part of developing AI applications such as expert systems, which apply the knowledge of a human expert to solve problems. 

What it entails: It involves the design and construction of AI systems, which involves encoding human expert knowledge into a form accessible to the computer.

Key tasks:

• Learning process: Obtaining information about human experts.
• Knowledge representation: Organizing and storing the data in a computer readable format.
• Knowledge deployment: Applying the knowledge to AI system problem solving.

Example applications:

• Expert Systems: An artificial intelligent (AI) application that emulates something that a person who is a professional expert in a given area would make, such as a financial advisor or a medical diagnosis system.
• Other AI applications: Knowledge engineering is also applied in other applications, including machine learning model development or natural language processing applications.

Skills involved:

• Information processing and categorizing.
• Natural language processing.
• Machine learning.
• Systems design.

Role in AI: Knowledge engineering gives knowledge that the AI systems require to be able to function, be intelligent agents, and make decisions. 

Major Subfields of AI - Computer Vision (CV)

 Computer Vision (CV) is a very significant branch of AI that can help machines to see and analyze visual data in form of pictures and videos. Its subfields are object recognition, image classification, and object detection which find application in self-driving cars, medical imaging and security systems. Others include video processing, which includes motion estimation and event detection and video tracking. 

The major fields in computer vision.

• Image and Object Recognition: Object, face and other visual image recognition and classification.
• Object Detection: Moving past classification and locating objects within an image, in addition to classifying them, which may be achieved by drawing bounding boxes around them.
• Image and Video Segmentation: The process of splitting an image or a video into parts in order to identify various objects or areas. This is semantic (labeling pixels with class names) and instance (labeling individual instances of an object) segmentation.
• Motion and Tracking: The process of studying how Objects move over the timeframe of a video and estimating the movement direction of objects, generally applied in tracking objects, or decoding actions.
• 3D and Pose Estimation: Recalling a 3D representation of a scene or 3D locating an object or a person.
• Image Restoration: Restoration or repairing damaged or noisy images to enhance the quality of an image. 

Common applications

• Autonomous Vehicles: The idea of autonomous cars is to allow cars to sense the world around them by identifying other cars, pedestrians, and road signs.
• Healthcare: Helping with the analysis of medical imaging, e.g. by detecting tumors in CT or diseases in X-rays.
• Robotics: Giving robots visual abilities in performing such tasks as inventory management, defects and route-finding.
• Security and Surveillance: Easily the facial recognition feature is provided, the anomalous behavior is detected and monitored over a large area.
• Manufacturing: It is used to automatically check the products to help to check defects and assume product quality.
• Augmented and Virtual Reality (AR/VR): following and placing the virtual objects in the real world.

Types of AI: Narrow AI vs General AI vs Super AI

Key components of AI

Intelligence has a broader context that reflects a deeper capability to comprehend the surroundings. However, for it to qualify as AI, all its components need to work in conjunction with each other. Let’s understand the key components of AI.

Machine learning: Machine learning is an AI application that automatically learns and improves from previous sets of experiences without the requirement for explicit programming.

Deep learning: Deep learning is a subset of ML that learns by processing data with the help of artificial neural networks.

Neural network: Neural networks are computer systems that are loosely modeled on neural connections in the human brain and enable deep learning.

Cognitive computing: Cognitive computing aims to recreate the human thought process in a computer model. It seeks to imitate and improve the interaction between humans and machines by understanding human language and the meaning of images.

Natural language processing (NLP): NLP is a tool that allows computers to comprehend, recognize, interpret, and produce human language and speech.

Computer vision: Computer vision employs deep learning and pattern identification to interpret image content (graphs, tables, PDF pictures, and videos).

Types of AI

Artificial Intelligence can be broadly divided into two categories: AI based on capability and AI based on functionality. Let’s understand each type in detail.

1. Narrow AI

Narrow AI is a goal-oriented AI trained to perform a specific task. The machine intelligence that we witness all around us today is a form of narrow AI. Examples of narrow AI include Apple’s Siri and IBM’s Watson supercomputer.

Narrow AI is also referred to as weak AI as it operates within a limited and pre-defined set of parameters, constraints, and contexts. For example, use cases such as Netflix recommendations, purchase suggestions on ecommerce sites, autonomous cars, and speech & image recognition fall under the narrow AI category.

2. General AI

General AI is an AI version that performs any intellectual task with a human-like efficiency. The objective of general AI is to design a system capable of thinking for itself just like humans do. Currently, general AI is still under research, and efforts are being made to develop machines that have enhanced cognitive capabilities.

3. Super AI

Super AI is the AI version that surpasses human intelligence and can perform any task better than a human. Capabilities of a machine with super AI include thinking, reasoning, solving a puzzle, making judgments, learning, and communicating on its own. Today, super AI is a hypothetical concept but represents the future of AI.

Now, let’s understand the types of AI based on functionality.

4. Reactive machines

Reactive machines are basic AI types that do not store past experiences or memories for future actions. Such systems zero in on current scenarios and react to them based on the best possible action. Popular examples of reactive machines include IBM’s Deep Blue system and Google’s AlphaGo.

5. Limited memory machines

Limited memory machines can store and use past experiences or data for a short period of time. For example, a self-driving car can store the speeds of vehicles in its vicinity, their respective distances, speed limits, and other relevant information for it to navigate through the traffic.

6. Theory of mind

Theory of mind refers to the type of AI that can understand human emotions and beliefs and socially interact like humans. This AI type has not yet been developed but is in contention for the future.

7. Self-aware AI

Self-aware AI deals with super-intelligent machines with their consciousness, sentiments, emotions, and beliefs. Such systems are expected to be smarter than a human mind and may outperform us in assigned tasks. Self-aware AI is still a distant reality, but efforts are being made in this direction.

History of Artificial Intelligence: From Turing to ChatGPT

 The history of AI has been a progression of conceptual work by Alan Turing in the 1950s, to modern systems such as ChatGPT, which have been characterized by stages of advancement and AI winters. Before 1956 The term artificial intelligence was coined in a workshop at Dartmouth in 1956, followed by the implementation of the so-called expert systems and early chatbots such as ELIZA in the 1960s and the resulting AI winter because of over-promising and under-delivering. The renaissance was accompanied by the development of machine learning and deep learning, and in 2022 the ChatGPT large language model was released, which proves to be able to converse on many subjects.

Early foundations (1950s)

• Alan Turing: He was viewed as a father of modern computing and artificial intelligence, and in 1950, he developed the so-called Turing Test to determine the power of a machine to behave in an intelligent way that would not be perceived as that of a human being.
• Dartmouth Workshop: In 1956, this workshop was the first workshop that used the term artificial intelligence which brought together the main researchers.
• First AI and Machine Learning: Scholars started to consider the ideas of artificial neural networks and what would come to be known as machine learning with researchers like the Shopper program on the EDSAC computer showing that its past search history could be used to learn. 

The period of AI winter and rule-based (1960s-1980s). 

• Expert Systems: The earliest so-called expert systems were developed in 1965, and they were aimed at mimicking the work of a human expert.
• Chatbots: Joseph Weizenbaum wrote a chatbot in 1966, called ELIZA, one of the earliest chatbots, which could use natural language processing to imitate a psychotherapist.
• First AI winter Research funding declined in the 1970s and 1980s because of a report on the lack of progress and the constraint of computational power, which became the first AI winter. 

Revival and modern AI (1990s-present)

Modern AI (1990s-present) Revival Revival art typically takes the form of art derived from an original artwork, with its distinctive methodological advances aligning with the revival era more closely.Revival and modern AI (1990s-present) Revival Art Revival art usually represents an art work based on an existing piece of art, and its new methodological developments can be more closely associated with the revival period.

Machine Learning and Deep Learning: The resurgence of AI research in the 1980s was due to the advancement of machine learning algorithms and the creation of deep learning.

Growth of Data: The proliferation of digital data to be used in training became a main point of improvement.

Deep Blue: this was the first milestone when in 1997 the Deep Blue computer played and won against the world chess champion Garry Kasparov.

AlphaGo: In 2016, AI succeeded the master of Go, Lee Sedol, and this feat was achieved by the AlphaGo created by Google, which led to the idea that AI is able to master a game that is believed to be much more complicated than chess.

ChatGPT: ChatGPT is a 2022 large language model that utilizes deep learning and the Generative Pre-trained Transformer (GPT) architecture to chat with humans and complete several text-based tasks, or ChatGPT.

Introduction to Artificial Intelligence – Definition, Scope & Goals

 Artificial Intelligence (AI) is the creation of computer systems that are capable of executing tasks that would otherwise be done by a human, namely learning, problem-solving, and perception. It has very broad applications, such as machine learning, computer vision, and natural language processing, and aims at both automatization of tasks and decision making and the ability of machines to perceive and react to human emotion.

Definition

• AI refers to the capacity of a computer or a program to selflessly replicate the human capacity to think, learn and perceive.
• It is a computer science discipline concerned with designing systems that are capable of addressing problems and performing tasks without being written code to do so on a case-by-case basis.
• Machine learning, pattern recognition, and neural networks are some of the technologies used by AI to analyze information and learn by the experience of its work over time. 

Scope

• Narrow AI (Weak AI): This is the most widespread type of AI whose purpose is to perform a task, which may include the voice assistants, such as Siri, recommendation systems, or image recognition programs.
• Machine Learning and Deep Learning: It is just a subset of AI that learns data with the help of algorithms. Machine learning is a process that works with structured data, and deep learning is a process that operates with multi-layer neural networks.
• Computer Vision: Computer vision is an area of study which teaches computers to perceive and understand what they see in the world allowing applications such as self-driving cars and medical image processing.
• Natural Language Processing (NLP): It is an AI that enables computers to read, comprehend, and produce human language. Examples would be translation services and chatbots.
• Others: This consists of cognitive modeling, pattern recognition, and such as AIOps (AI for IT Operations). 

Goals

• Task Automation: To reduce repetitive and complex jobs in the digital and physical worlds, to liberate human labor, employ it on more creative or strategic work.
• Improved decision-making: To make faster, more accurate, and reliable decisions based on data due to further analytics and predictions.
• Error Reduction: To cut down human error within the processes either by giving them guidance or making them one hundred percent automated, particularly in areas that are very precise such as healthcare.
• Solving Complex Problems: To solve problems that are outside of the human abilities because of their complexity or size.
• Human like Interaction: To develop systems that can comprehend human attitudes and intentions in order to facilitate more advanced and sympathetic interactions as in the case of the “Theory of Mind Objective. 

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Introduction to Artificial Intelligence (AI Fundamentals) – Complete Tutorial, Notes & Syllabus (APSCHE UG 2025-26)

 Introduction to Artificial Intelligence (AI Fundamentals)

Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century, shaping how we live, learn, and work. The “Introduction to Artificial Intelligence (AI Fundamentals)” course is designed especially for undergraduate students to build a strong foundation in the core concepts, applications, and ethical dimensions of AI.

This course introduces students to the history, evolution, and subfields of AI — including Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Robotics, and Knowledge Engineering. It explains how AI systems mimic human intelligence through perception, reasoning, and decision-making.

Learners will explore real-world AI applications across key sectors like healthcare, agriculture, education, finance, and transportation. Beyond technical knowledge, the course emphasizes responsible AI development, addressing crucial topics such as bias, fairness, transparency, privacy, inclusivity, and sustainability in AI systems.

A unique feature of this syllabus is the integration of Generative AI and Prompt Engineering, enabling students to experiment with tools like ChatGPT, Gemini, Hugging Face, and SlidesGPT for content generation, creative design, and interactive learning. These hands-on exercises make the course both engaging and industry-relevant.

By the end of this course, students will:

• Understand the fundamental concepts and subfields of Artificial Intelligence.
• Analyze how AI impacts diverse industries and daily life.
• Evaluate ethical and societal challenges of AI.
• Gain practical exposure to Generative AI and Prompt Engineering.
• Develop confidence to apply AI tools for innovation and research.

Whether you’re an aspiring data scientist, a software engineer, or simply curious about how AI works, this course will help you think critically, experiment creatively, and understand the power of intelligent systems.

The three units given in the syllabus is divided in five units for easy understing of students.

Unit 1: AI and Its Subfields

Overview: Introduction to AI, historical evolution, definitions, challenges, and subfields.

1. Introduction to Artificial Intelligence – Definition, Scope & Goals

2. History of Artificial Intelligence: From Turing to ChatGPT

3. Types of AI: Narrow AI vs General AI vs Super AI

4. Major Subfields of AI Explained:

   • Knowledge Engineering

   • Machine Learning (ML)

   • Computer Vision (CV)

   • Natural Language Processing (NLP)

   • Robotics

5. Industry Applications of AI – Healthcare, Agriculture, Education & Beyond

6. Challenges in Building Intelligent Systems – Data, Ethics, and Resources

7. Case Study: How AI is Powering Smart Cities and Digital India

Unit 2: Applications of AI

Overview: Real-world impact of AI across sectors.

1. AI in Healthcare – Early Diagnosis & Drug Discovery

2. AI in Finance – Fraud Detection, Risk Analysis, and Algorithmic Trading

3. AI in Retail – Recommendation Systems and Customer Analytics

4. AI in Agriculture – Crop Prediction and Smart Irrigation

5. AI in Education – Personalized Learning and Virtual Tutors

6. AI in Transportation – Autonomous Vehicles and Traffic Management

7. Future of AI Applications – Quantum AI and Edge Computing

Unit 3: Bias, Fairness, and Ethics in AI Systems

Overview: Understanding the social, ethical, and governance aspects of AI.

1. What Is AI Ethics? Principles Every AI Developer Should Know

2. Understanding Bias in AI – Causes and Consequences

3. Fairness in AI Systems – Building Trustworthy Models

4. Transparency and Explainability in AI Decisions

5. Accountability and Privacy in Machine Learning Models

6. Inclusivity and Sustainability in AI Development

7. Robustness and Reliability – Making AI Systems Safe

Tutorial Add-Ons:

• Examples of biased outputs from ChatGPT, DALL-E, etc.

• Classroom activity: Detecting gender bias in AI image generation

Unit 4: AI in Research, Generative AI & Prompt Engineering

Overview: Exploring Generative AI tools and their research relevance.

1. Role of AI in Modern Scientific Research and Experimentation

2. What Is Generative AI? Understanding ChatGPT, Gemini, and Hugging Face

3. Generative AI vs Traditional AI – Key Differences Explained

4. Introduction to Prompt Engineering – The Art of Talking to AI

5. Prompt Design Strategies: Zero-Shot, Few-Shot, and Chain-of-Thought

6. Future Trends in AI Research – Multimodal and Federated AI

7. Hands-on Tutorial: Writing Effective Prompts for Better AI Results

Unit 5: Applications of Prompt Engineering

Overview: Practical use-cases of prompt engineering in education, business, and creative domains.

1. Prompt Engineering in Education – Smart Content Creation & Tutoring

2. How Businesses Use Prompt Engineering for Marketing and Automation

3. AI for Creative Writing – Storytelling, Poetry, and Idea Generation

4. AI for Design and Branding – Using Canva Magic Media & Adobe Firefly

5. Writing YouTube Video Scripts with AI Tools

6. Creating PowerPoint Presentations with SlidesGPT and Tome AI

7. Designing Thumbnails and Visuals with Generative AI

8. Prompt Engineering for Students – Learn by Doing

ANU PG Exams November 2025 – Revised Notification, Fee Details & Important Dates

 Acharya Nagarjuna University (ANU), Nagarjuna Nagar – Guntur, has released a Revised Notification for PG Examinations (Regular & Supplementary) for all P.G. M.A., M.Sc., MHRM, M.Com., MBA, MCA, M.Ed., M.Li.Sc., and Professional Courses for the academic year 2024-25.

Exams are scheduled to commence from 14-11-2025.

Courses Covered Under ANU PG Exam Notification 2025

The notification applies to:

• All PG Programs (M.A., M.Sc., MHRM, M.Com., M.Ed., MBA, MCA, M.Li.Sc.)

• Professional Courses: MBA (HA), MBA (HM), MBA (IB), MBA (TTM)

• Vocational Programs: M.VOC FP&QM, M.VOC H&LC, M.VOC H&LG

• PG Diploma: Analytical Chemistry Techniques for Pharmaceuticals

• M.Sc Nano-Technology, Soil Science & Agricultural Chemistry, and others

Important Dates for ANU PG Examinations 2025

Event	Date
Last date for payment of exam fee without fine	29-10-2025 (4:00 PM)
Last date for payment of exam fee with ₹100 fine	30-10-2025
Last date for submission of gallies to ANU (Online & Manual)	31-10-2025
Commencement of PG 3rd Semester Examinations (M.A, M.Sc, MHRM, M.Com, M.Ed, MBA, MCA etc.)	14-11-2025
Practical Examinations to be completed on or before	12-12-2025
Last date for submission of Internal / MOOCs / Practical Marks Online	15-12-2025

ANU PG Examination Fee Details (Academic Year 2024–25)

Course	Whole Exam Fee (₹)	Single Paper	Two Papers	Three Papers	Four or More Papers	Project/Viva Fee	Betterment
M.Tech (Bio-Tech)	3480	860	1780	2640	3480	520	680
M.A., M.Sc., MHRM, M.Li.Sc.	980	520	700	860	980	520	680
5 Years MBA (IB) / M.Sc. Nano Tech	980	520	700	860	980	520	680
M.Com., MBA, MBA (HA), MBA (TTM)	1100	520	700	860	980	520	680
Certificate / PG Diploma Courses	1100	550	710	960	980	520	—
M.Ed.	1540	550	710	960	1540	520	680
MCA	1090	520	700	860	980	520	680

Instructions for Colleges & Students

• All Principals of affiliated colleges must collect the examination fee and upload gallies through the online system on or before 31-10-2025.

• Certified copies of candidate lists (course-wise) must be sent along with attendance and “No Dues Certificates.”

• Internal and Practical Marks must be uploaded online before 15-12-2025.

• Hall tickets will be issued only after verifying eligibility and absence of malpractice.

• Students should complete their MOOC’s courses during the current semester as per ANU guidelines.

Official Information

• University Website: www.anu.ac.in

• Office: Controller of Examinations, Acharya Nagarjuna University, Nagarjuna Nagar – 522510

• Helpline: 0863-2346777

• ANU PG Exam Notification 2025,
• Acharya Nagarjuna University Revised PG Notification November 2025,
• ANU MA MSc MCom MBA MCA Exam Dates 2025,
• ANU PG Examination Fee Details 2025,
• ANU M.Ed, MBA, MCA Exam Notification 2025,
• www.anu.ac.in PG Exams 2025,
• ANU Internal Marks Submission 2025,
• Acharya Nagarjuna University Time Table 2025

ANU Revaluation Fee Date Extended – M.Sc, B.Tech, MBA, Diploma Courses (Revised Schedule October 2025)

 Acharya Nagarjuna University (ANU), Nagarjuna Nagar, Guntur has officially extended the last date for payment of Revaluation Fee for certain PG, UG, and Professional courses. This revision benefits students who missed the earlier deadline.

Official Circular Date: 24-10-2025

Revised Last Date for Payment of Revaluation Fee

Sl. No.	Course	Existing Last Date	Revised Last Date
1	2nd Semester M.Sc Chemistry	24-10-2025	31-10-2025
2	2nd Semester Diploma in Photography	24-10-2025	31-10-2025
3	1st Semester Supplementary B.Tech	28-10-2025	31-10-2025
4	2nd Semester MBA (International)	28-10-2025	31-10-2025

Important Instructions for Colleges and Students

• All Principals of University Colleges and affiliated colleges are instructed to take necessary action as per the revised schedule.

• Students are advised to pay the revaluation fee before 31-10-2025 through their respective colleges to avoid missing the opportunity.

• This circular applies to PG and Professional Courses under Acharya Nagarjuna University.

Official Reference

University: Acharya Nagarjuna University
Location: Nagarjuna Nagar – 522510, Guntur Dt., Andhra Pradesh
Website: www.anu.ac.in
Office Contact: 0863-2346777

SEO Keywords

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ANU LL.B 5th & 9th Semester Exam Notification November 2025 – Fee Details, Last Date, and Schedule

 Acharya Nagarjuna University (ANU), Nagarjuna Nagar, Guntur, has released the official LL.B Examination Notification for the 5th Semester of 3-Year LL.B and 9th Semester of 5-Year LL.B courses for the academic year 2024-25. The exams are scheduled to commence from 18-11-2025.

Important Dates for LL.B Exams November 2025

Event	Date
Last date for payment of exam fee without fine	01-11-2025
Last date for payment of exam fee with ₹100 fine	03-11-2025
Last date for submission of gallies through Online	04-11-2025
Last date for submission of gallies to C.E. Office	04-11-2025
Exams commence from	18-11-2025

ANU LL.B Exam Fee Details (3-Year & 5-Year Courses)

Particulars	Fee (₹)
Whole examination fee	960
Fee for single paper	520
Fee for two papers	690
Fee for three papers	860
Fee for four or more papers	960
Betterment fee (per paper)	680

Note:

• The consolidated Online Challan for the 5th and 9th semesters must be taken on or before 01-11-2025 (without fine) and 03-11-2025 (with fine).

• Submission of gallies to the Controller of Examinations (C.E.) Office must be completed on or before 04-11-2025.

Instructions to Principals

• Colleges should upload exam data online using the Challan SBI A/c No. 30908794589.

• Gallies must be sent to the university before 04-11-2025.

• Internal Assessment marks should be sent before commencement of theory exams, and Practical marks must be uploaded immediately after completion of practical exams.

• Hall tickets will be issued only after verifying eligibility and confirming that the student has no disqualification or malpractice cases.

Key Points for Students

1. Students must ensure they pay the exam fee before the due date to avoid penalties.

2. Hall tickets can be downloaded from the official website after approval.

3. For betterment or backlog papers, fees should be paid separately as per the notification.

4. Internal and practical marks will be uploaded online by the respective college principals.

Official Links

• University Website: www.anu.ac.in

• Controller of Examinations Office: Acharya Nagarjuna University, Nagarjuna Nagar – 522510, Guntur Dt., Andhra Pradesh

1. ANU LL.B Exam Notification 2025,
2. ANU LL.B 5th Semester Exam Fee Details,
3. Acharya Nagarjuna University Law Exams November 2025,
4. ANU 3-Year LL.B and 5-Year LL.B Exam Time Table 2025,
5. ANU Law Course Exam Dates,
6. ANU LL.B Fee Last Date November 2025,
7. ANU Exam Updates 2025,
8. ANU Hall Ticket Download,
9. ANU LL.M Notifications,

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ANU Pharm D Reg Exams April 2025 Revaluation Results

 ANU Pharm D Reg Exams April 2025 Revaluation Results  are now available, the candidates who are looking for results can check their results from here

Check your results from below links

• II/VI PHARM.D REGULAR EXAMINATIONS APRIL-2025 REVALUATION RESULTS.
• III/VI PHARM.D REGULAR EXAMINATIONS APRIL-2025 REVALUATION RESULTS.
• IV/VI PHARM.D REGULAR EXAMINATIONS APRIL-2025 REVALUATION RESULTS.
• V/VI PHARM.D REGULAR EXAMINATIONS APRIL-2025 REVALUATION RESULTS.

ANU UG 2ND SEMESTER REGULAR AND SUPPLY EXAMS APRIL 2025 REVALUATION RESULTS

 ANU UG 2ND SEMESTER REGULAR AND SUPPLY EXAMS APRIL 2025 REVALUATION RESULTS are now available, the candidates who are looking for results can check their results from here

Check your results from below link

ANU UG 2ND SEMESTER REGULAR AND SUPPLY EXAMS APRIL 2025 REVALUATION RESULTS