Dr.V.Shanmukha Rao, Professor & Head, CSE-AI
Department of CSE-ARTIFICIAL INTELLIGENCE - (AIIM) ARTIFICIAL INNOV - MINDS CLUB INAUGURATION
PSCMRCET MANAGEMENT , PRINCIPAL, HEADS OF THE DEPARTMENT
Paddlers Drowning Detection System Using Ultralytics-Yolo v8 in Deep Learning
Abstract -This paper presents the development and implementation of a his paper presents the development and implementation of a deep learningbased approach for real-time detection of drowning swimmers using the YOLO v8 (You Only Look Once version 8) object detection framework. Drowning remains a significant cause of mortality worldwide, necessitating effective surveillance and rapid response systems. Leveraging the capabilities of YOLO v8, we train a neural network model on a custom dataset comprising annotated images of drowning scenarios. The dataset encompasses diverse environmental conditions, swimmer orientations, and occlusions to enhance the model's robustness. Our approach utilizes transfer learning to fine-tune the pre-trained YOLO v8 model on the drowning swimmer detection task, achieving high accuracy and efficiency. We evaluate the performance of our proposed method on both synthetic and real-world datasets, demonstrating its effectiveness in detecting drowning swimmers with high precision and recall rates. Furthermore, we conduct comparative experiments with existing drowning detection methods to validate the superiority of our approach in terms of accuracy, speed, and real-time applicability. The proposed system holds promise for enhancing water safety measures by enabling timely detection and intervention in drowning incidents.
FUTURE FORGE FORUM IN ACADEMIA
Abstract -This paper presents the development and implementation of a comprehensive college community platform, termed ForgeNet, built using the MERN (MongoDB, Express.js, React.js, Node.js) stack. ForgeNet aims to facilitate student engagement, academic support, and career development within the college ecosystem. The platform encompasses features such as student networking, buying and selling collegerelated products, accessing exam references, exploring job postings, and receiving mentorship from senior peers. Leveraging modern web technologies, ForgeNet provides a seamless and intuitive interface for students to connect, collaborate, and thrive academically and professionally. The paper outlines the system architecture, implementation details, and evaluation of ForgeNet, demonstrating its efficacy in fostering a vibrant and supportive college community. Through ForgeNet, students can access a centralized hub for academic and career resources, enhancing their overall college experience and readiness for the future Keywords: College Community, MERN Stack, Student Engagement, Exam Resources, Job Postings, Mentorship, Student Connectivity, User Interface, User Experience.
Journal of Nonlinear Analysis and Optimization Vol. 15, Issue. 1, No.12 : 2024 ISSN : 1906-9685
AI SIMULATED FAUX IMAGE DETECTION SYSTEM USING DEEP LEARNING
ABSTRACT – Although biometric technology is essential for identifying people, thieves are always evolving to avoid being caught. We are using a state-of-the-art method called Deep Texture Features extraction from photos to tackle this problem. Building a Convolutional Neural Network (CNN) is our method for efficiently utilizing this technology. Known as LBPNET, this CNN-based model emphasizes the use of the Local Binary Pattern (LBP) methodology for feature extraction, making it stand out as a cuttingedge approach in the industry. To counter the spread of fake face photos in identification systems, we train the machine learning model to understand LBP descriptors taken from images. Our technique promises to improve the accuracy and dependability of facial recognition systems by integrating CNN technology with LBP descriptors, hence reducing the risk associated with fraudulent modifications to physical and psychological traits.
KEYWORDS: Deep textures, CNN, NLBPNet, LBPNet, LBP descriptor images.
Trace mortal pursuit framework using AI
ABSTRACT - Computer interfaces novelties are
noticeable to expand AI based technology. Human actions and movements are
recorded, tracked, and noticed with the help of AI based expert systems. NLP,
ML, and its techniques are owned for tracking of human behaviours, according to
the cautions that are generated in crucial time of situations will assist the
mortals. To restrain emergency situations, AI based set-up and its mode are
popular in human way of life. Surveillances gadgets are required
around-the-clock to track record in the form of facts that are analysed using
computer visionary techniques, NLP is utilized to recognize the human behaviour
speech to diagnosis the circumstances and act according to posies. RNN with
LSTM techniques are exercised and examine to execute the framework.
"TRACE MORTAL PURSUIT FRAMEWORK USING AI", Dr.V.Shanmukha Rao,Md.Imran,D.S. Srinivas,D. Varun Prasad, Juni Khyat ISSN: 2278-4632 (UGC Care Group I Listed Journal) Vol-13, Issue-04, No.06, April : 2023 Page | 17 DOI: 10.36893.JK.2023.V13I04N16.0017-0022
http://junikhyatjournal.in/no_1_Online_23/3s_apr.pdf
http://junikhyatjournal.in/no_1_Online_23.html
Analyzing of human facial bearings
from facial emotion detection framework using Deep Learning
ABSTRACT-
Aspirations are increased perilously with the contemporary technology in these
days; Deep learning techniques are driven to develop various types of
applications in and around computer vision. HCI systems are designed to analyze
various human emotions that will help to study and analyze human behaviors. In
Artificial intelligence, CNN and DNN models are pinpoint and trained to test to
detect the emotions of human face and extracting their facial features. Mankind
emotions such as annoyance, aversion, panic, satisfaction, unhappiness,
revelation and so forth. Features are classified and analyzed using emotion
recognition methodologies. The frameworks will be recognized and evaluated to
generate the various results of samples that will help in investigating the
human facial bearings and its instances that are used in a wide variety of HCI
applications.
Keywords:
HCI, CNN, DNN, Framework, bearings, instances
"ANALYZING OF HUMAN FACIAL BEARINGS FROM FACIAL EMOTION DETECTION FRAMEWORK USING DEEP LEARNING", Dr.V.Shanmukha Rao,Md.Imran,D.S. Srinivas,D. Varun Prasad, Dogo Rangsang Research Journal UGC Care Group I Journal, ISSN : 2347-7180 Vol-13, Issue-4, No. 20, April 2023 Page | 244 DOI: 10.36893.DRSR.2023.V13I0.0244-0249
https://www.journal-dogorangsang.in/no_1_Online_23/37s.pdf
https://www.journal-dogorangsang.in/no_1_Online_23.html
DETECTION OF RANSOMWARE ATTACKS IN NETWORK USING MACHINE LEARNING
1.ABSTRACT
The development of computer and communication technology has resulted in considerable changes from the past. Despite the fact that utilising new inventions benefits people, organisations, and governments greatly, some people are prejudiced against them. For instance, information security in file systems, information accessibility, and so on.Due to several groups, including the criminal underworld, professionals, and digital activists, including dread of the digital world, which has caused many issues for individuals and organisations, has reached the point where it may jeopardise national and open security. Intrusion Detection Systems (IDS) were created as a result to maintain a safe distance from internet threats. The new CICIDS2017 dataset was utilised to train the Support vector machine (SVM) computations, which are currently being used to identify port sweep efforts. Instead of SVM, we might use other algorithms like CNN, ANN, and random forest. Keywords: data security, information accessibility, digital fear, intrusion detection systems.
1 B Ramakrishna 2 A Ajith Reddy 3 B Srinivas 4 Dr V Shanmukha Rao
Download Link: https://ijsrem.com/download/
Please find your published Research Paper here
https://ijsrem.com/
Seminar Presentation on Research Trends in Artificial Intelligence on 22-06-2022, at
Department of Information Technology
Vijaya Institute of Engineering and Technology, Vijayawada
Mrs.Y.Vijaya, Head of the Department , Information Technology, VITW.
Blog Mining Using Search Engine Optimization and Machine learning
The blog is a regularly updated website or web page, typically run by an individual or small group, that is written in an informal or conversational style. Blogs consist of a series of posts where posts are archived, and are usually sorted into categories. Bloggers identify the sentiments, both positive and negative opinions about the topic to understand and present public views in detail. Readers can browse these categories through the blog to read older entries. It does typically involve searching and analyzing blogs in order to generate additional insights and acts asan information source for the user’s ideas. Blog Mining provides a capability of processing large amounts of text data effectively, blog mining can be a valuable method for gaining insights into a given topic. This study of blog mining is used to analyze and search the online blog posts relevant contents in a quite simpler fashion In this, providing three main features to the website, where it will most helpful to Blogger’s and Reader’s as well, they are:
1. Blog Mining 2. Text Summarization 3. Search Engine Optimization
Providing a platform to Blogger’s and Reader’s. Where the blogger will continually post new content orinformation in the Blog and readers will check them frequently and gain knowledge or information from the blogs. So, we created a website for both of them, where bloggers have a separate account in the website, they can login and update/write the content and readers can go to the home page directly to view the blogs of bloggers. Here we are creating a Techie Blog website.
Keywords- Blog, Blog-mining, Machine learning, Latent Semantic Analysis, Search Engine Optimization, Hummingbird, Panda & Hybridization.
“Blog Mining Using Search Engine Optimization and Machine learning”, Dr.V. Shanmukha Rao1, Vikram Pagadala2 , MD. Ibraheem3 , CH. Roop Kumar4, International Journal of Scientific Research in Engineering and Management (IJSREM), ISSN: 2582-3930, Volume: 06 Issue: 06 | June – 2022, DOI: 10.55041/IJSREM14235,
https://ijsrem.com/download/blog-mining-using-search-engine-optimization-and-machine-learning/
III Convocation of the University in virtual presence of Shri Biswa Bhusan Harichandan Garu,
Hon'ble Governor of Andhra Pradesh and Chancellor of the University.
21st May 2022, Kurnool,AP
Presented & Resource person
Learning Techniques in Artificial Intelligence - National level Conference
Christ College- Thiruvallur, Tamilanadu
19-05-2022
Techno "RK" 2K22
Classification of brain tumours using artificial neural networks
ABSTRACT
Magnetic Resonance (MR) brain Image is very important for medial analysis and diagnosis. These images are generally measured in radiology department to measure images of anatomy as well as the general physiological process of the human body. In this process magnetic resonance imaging measurement are used with a heavy magnetic field, its gradients along with radio waves to produce the pictures of human organs. MR brain image is also used to identify any blood clots or damaged blood veins in the brain. A counterfeit neural organization is a nonlinear information handling model that have been effectively used preparation models for tackling administered design acknowledgment assignments because of its capacity to sum up this present reality issues. Artificial Neural Networks (ANN) is used to classify the given MR brain image having Benign or malignant tumour in the brain. Benign tumours are generally not cancerous tumours. These are also not able to grow or spread in the human body. In very rare cases they may grow very slowly. Once it is eliminated, they do not come again. On the other hand, malignant tumours are cancer tumours. These tumour cells are grown and also easily spread to other parts of the human body. Benign also known as Harmless. These are not destructive. They either can't spread or develop, or they do as such leisurely. On the off chance that a specialist eliminates them, they don't by and large return. Premalignant In these growths, the cells are not yet harmful, however they can possibly become threatening. Malignant also known as threatening. Malignant growths are destructive. The cells can develop and spread to different pieces of the body. In our proposed framework initially, it distinguishes Wavelet Transform to separate the highlights from the picture. Subsequent to separating the highlights it incorporates tumour shape and power attributes just as surface highlights are distinguished. Finally, ANN to group the information highlights set into Benign or malignant tumour. The main purpose as well as the objective is to identifying the tumours weather it belongs to Benign or Malignant.
https://acta.imeko.org/index.php/acta-imeko/article/view/IMEKO-ACTA-11%20%282022%29-01-35
https://acta.imeko.org/index.php/acta-imeko/issue/view/39
Course Name: Distributed Systems
Program
:
B.Tech
Reg: R16
Class
:IV
B.Tech-2
Semester: Even
OBJECTIVES:
• Provides an
introduction to the fundamentals of distributed computer systems, assumingthe
availability of facilities for data transmission, IPC mechanisms in
distributedsystems, Remote procedure calls.
• Expose students to
current technology used to build architectures to enhance distributed Computing
infrastructures with various computing principles
OUTCOMES:
• Develop a familiarity
with distributed file systems.
• Describe important characteristics of distributed systems and the salient
architectural
features of such systems.
• Describe the features and applications of important standard protocols which
are used in
distributed systems.
• Gaining practical experience of inter-process communication in a distributed
environment
University Syllabus
UNIT-I:
Characterization of
Distributed Systems: Introduction, Examples of Distributed Systems,
Resource Sharing and the Web, Challenges.
System Models: Introduction, Architectural Models- Software Layers, System
Architecture,
Variations, Interface and Objects, Design Requirements for Distributed
Architectures,
Fundamental Models- Interaction Model, Failure Model, Security Model.
UNIT-II:
Interprocess Communication: Introduction, The API for the Internet Protocols-
The
Characteristics of Interprocess communication, Sockets, UDP Datagram
Communication, TCP
Stream Communication; External Data Representation and Marshalling; Client
Server
Communication; Group Communication- IP Multicast- an implementation of group
communication, Reliability and Ordering of Multicast.
UNIT-III:
Distributed Objects and Remote Invocation: Introduction, Communication between
Distributed Objects- Object Model, Distributed Object Modal, Design Issues for
RMI,
Implementation of RMI, Distributed Garbage Collection; Remote Procedure Call,
Events and
Notifications, Case Study: JAVA RMI
UNIT-IV:
Operating System Support: Introduction, The Operating System Layer, Protection,
Processes
and Threads –Address Space, Creation of a New Process, Threads.
UNIT-V:
Distributed File Systems: Introduction, File Service Architecture; Peer-to-Peer
Systems:
Introduction, Napster and its Legacy, Peer-to-Peer Middleware, Routing
Overlays.
Coordination and Agreement: Introduction, Distributed Mutual Exclusion, Elections,
Multicast Communication.
UNIT-VI:
Transactions & Replications: Introduction, System Model and Group
Communication,
Concurrency Control in Distributed Transactions, Distributed Dead Locks,
Transaction
Recovery; Replication-Introduction, Passive (Primary) Replication, Active
Replication.
TEXT BOOKS:
1. Ajay D Kshemkalyani,
Mukesh Sighal, “Distributed Computing, Principles, Algorithms and
Systems”, Cambridge
2. George Coulouris, Jean Dollimore, Tim Kindberg, “Distributed Systems-
Concepts and Design”,
Fourth Edition, Pearson Publication
REFERENCE BOOKS
Distributed-Systems-Principles-Paradigms-Tanenbaum
PHI
Pre-Class :
Videos, E-books, Web links, Case Studies etc…
In-Class : Explanation
on concept, discussion, Poll, doubts clarification, PPT, Demo etc..
Post-Class : Discussion
Forum, Review on topic, Assessment, Quiz, Notes etc….
Activity / Schedule
|
CLASS SL.
NO |
CONCEPT |
OBJECTIVES |
PRE-CLASS |
IN-CLASS |
POST-CLASS |
|
unit-1 |
Understanding the fundamentals of distributed computer
systems |
Text Book: Textbook PDF upload in LMS tool |
Discussion on pre-requisites (10 Min) |
Discussion Forum on the topic in the group/
Review on the topic, Share material(PPT, Notes pdf,Textbook pdf) on the
topic. Presentation video available in canvas student account |
|
|
1 |
Characterization of Distributed Systems: Introduction |
||||
|
2 |
Examples of Distributed Systems |
||||
|
3 |
Examples of Distributed Systems |
||||
|
4 |
Resource Sharing and the Web |
||||
|
5 |
Challenges. |
||||
|
6 |
System Models: Introduction |
||||
|
7 |
Architectural Models |
||||
|
8 |
Software Layers |
||||
|
9 |
System Architecture |
||||
|
10 |
Variations, Interface and Objects |
||||
|
11 |
Design Requirements for Distributed Architectures |
||||
|
12 |
Fundamental Models- Interaction Model |
||||
|
13 |
Failure Model |
||||
|
14 |
Security Model. |
||||
|
15 |
Weekly Test |
||||
|
unit-2 |
Available facilities for data transmission |
Text Book: Textbook PDF upload in LMS tool |
Discussion on pre-requisites (10 Min) |
Discussion Forum on the topic in the group/
Review on the topic, Share material(PPT, Notes pdf,Textbook pdf) on the
topic. Presentation video available in canvas student account |
|
|
16 |
Interprocess Communication: Introduction |
||||
|
17 |
The API for the Internet Protocols |
||||
|
18 |
Characteristics of Interprocess communication |
||||
|
19 |
Sockets |
||||
|
20 |
UDP Datagram Communication |
||||
|
21 |
TCP Stream Communication |
||||
|
22 |
External Data Representation and Marshalling |
||||
|
23 |
Client Server Communication |
||||
|
24 |
Group Communication |
||||
|
25 |
IP Multicast- an implementation of group communication, |
||||
|
26 |
Reliability and Ordering of Multicast. |
||||
|
unit-3 |
Understanding the fundamentals of IPC mechanisms in
distributed |
Text Book: Textbook PDF upload in LMS tool |
Discussion on pre-requisites (10 Min) |
Discussion Forum on the topic in the group/
Review on the topic, Share material(PPT, Notes pdf,Textbook pdf) on the
topic. Presentation video available in canvas student account |
|
|
27 |
Introduction Distributed Objects and Remote Invocation |
||||
|
28 |
Communication between |
||||
|
29 |
Object Model |
||||
|
30 |
Distributed Object Modal |
||||
|
31 |
Design Issues for RMI |
||||
|
32 |
Implementation of RMI |
||||
|
33 |
Distributed Garbage Collection |
||||
|
34 |
Remote Procedure Call |
||||
|
35 |
Events and |
||||
|
36 |
Case Study: JAVA RMI |
||||
|
37 |
JAVA RMI |
||||
|
unit-4 |
Able to understand the current technology used to build
architectures |
Text Book: Textbook PDF upload in LMS tool |
Discussion on pre-requisites (10 Min) |
Discussion Forum on the topic in the group/
Review on the topic, Share material(PPT, Notes pdf,Textbook pdf) on the
topic. Presentation video available in canvas student account |
|
|
38 |
Operating System Support |
||||
|
39 |
The Operating System Layer |
||||
|
40 |
Protection |
||||
|
41 |
Processes |
||||
|
42 |
Address Space |
||||
|
43 |
Creation of a New Process |
||||
|
44 |
Threads |
||||
|
45 |
Distributed File Systems |
||||
|
46 |
File Service Architecture |
||||
|
47 |
Peer-to-Peer Systems |
||||
|
48 |
Napster and its Legacy |
||||
|
49 |
Peer-to-Peer Middleware |
||||
|
50 |
Routing Overlays |
||||
|
51 |
Coordination and Agreement |
||||
|
52 |
Distributed Mutual Exclusion |
||||
|
53 |
Elections |
||||
|
54 |
Multicast Communication |
||||
|
unit-5 |
To study the salient architectural |
Text Book: Textbook PDF upload in LMS tool |
Discussion on pre-requisites (10 Min) |
Discussion Forum on the topic in the group/
Review on the topic, Share material(PPT, Notes pdf,Textbook pdf) on the
topic. Presentation video available in canvas student account |
|
|
55 |
Introduction |
||||
|
56 |
Distributed File Systems |
||||
|
57 |
File Service Architecture |
||||
|
58 |
Peer-to-Peer Systems |
||||
|
59 |
Napster and its Legacy |
||||
|
60 |
Peer-to-Peer Middleware |
||||
|
61 |
Routing Overlays |
||||
|
62 |
Coordination and Agreement |
||||
|
63 |
Distributed Mutual Exclusion |
||||
|
64 |
Elections |
||||
|
65 |
Multicast Communication |
||||
|
unit-6 |
To enhance distributed Computing
infrastructures with various computing principles |
Text Book: Textbook PDF upload in LMS tool |
Discussion on pre-requisites (10 Min) |
Discussion Forum on the topic in the group/
Review on the topic, Share material(PPT, Notes pdf,Textbook pdf) on the
topic. Presentation video available in canvas student account |
|
|
66 |
Introduction |
||||
|
67 |
Transactions & Replications |
||||
|
68 |
System Model and Group Communication |
||||
|
69 |
Concurrency Control in Distributed Transactions |
||||
|
70 |
Distributed Dead Locks |
||||
|
71 |
Transaction |
||||
|
72 |
Recovery |
||||
|
73 |
Replication-Introduction |
||||
|
74 |
Passive (Primary) Replication |
||||
|
75 |
Active Replication |
||||
|
76 |
Revision |
Department of CSE-ARTIFICIAL INTELLIGENCE - (AIIM) ARTIFICIAL INNOV - MINDS CLUB INAUGURATION PSCMRCET MANAGEMENT , PRINCIPAL, HEADS OF THE...
