Attribute Classification
Download as pptx, pdf0 likes60 views
This document appears to list various attributes without context or descriptions. It includes gender classifications like "Female" and "Male" as well as descriptions of people such as "Three Person" and "Two Person". It also lists objects like "Helmet", "Mask", and "Carrying Bag".
1 of 1
Download to read offline
Ad
Recommended
Introduction to Data Structure 
Introduction to Data Structure Kamal Singh Lodhi Data structures organize data in computer memory. Arrays and lists are common data structures. Arrays use indexes to access fixed size elements, while lists allow insertions/deletions anywhere. Summing and multiplying arrays and matrices are important algorithms. Summing an array involves iterating through elements and adding to a running total. Multiplying matrices involves nested loops to calculate each element as the sum of products of corresponding row and column elements from the two matrices.
Stack Algorithm
Stack AlgorithmKamal Singh Lodhi The document discusses stacks and their applications. Stacks follow LIFO (last-in, first-out) order and allow insertions and removals only from the top. Common applications include evaluating arithmetic expressions by converting them to postfix notation and solving problems like Tower of Hanoi. The algorithms for push, pop, infix to postfix conversion and postfix evaluation are presented.
Data Structure (MC501)
Data Structure (MC501)Kamal Singh Lodhi The document provides an overview of several sorting algorithms, including insertion sort, bubble sort, selection sort, and radix sort. It describes the basic approach for each algorithm through examples and pseudocode. Analysis of the time complexity is also provided, with insertion sort, bubble sort, and selection sort having worst-case performance of O(n^2) and radix sort having performance of O(nk) where k is the number of passes.
Cs501 trc drc
Cs501 trc drcKamal Singh Lodhi This document discusses tuple relational calculus (TRC) and domain relational calculus (DRC), which are nonprocedural query languages for relational databases. It provides examples of queries written in TRC and DRC on sample bank data. It also covers the syntax and semantics of predicates in TRC/DRC, including quantifiers, connectives, and safety conditions for queries to return finite result sets. Expressive power and equivalence between the basic relational algebra and the two calculi are also discussed.
Cs501 transaction
Cs501 transactionKamal Singh Lodhi This document discusses transaction management in databases. It defines a transaction as a unit of program execution that accesses and updates data items. Transactions must satisfy the ACID properties of atomicity, consistency, isolation, and durability to maintain data integrity. Atomicity ensures that transactions are fully completed or rolled back. Consistency means transactions preserve the consistency constraints of the database. Isolation ensures transactions execute independently without interfering with each other. Durability means transaction changes persist even after failures. The document discusses various concurrency control techniques like serializability to coordinate concurrent transaction execution while preserving isolation.
Cs501 rel algebra
Cs501 rel algebraKamal Singh Lodhi This document discusses query languages and relational algebra operations. It introduces relational algebra as a procedural query language. The basic relational algebra operations are selection, projection, union, set difference, cartesian product, and rename. Examples are provided to illustrate each operation. Additional operations like join, outer join, division and aggregation are also discussed. The document concludes with a discussion of database modification operations like deletion, insertion and updating.
Cs501 mining frequentpatterns
Cs501 mining frequentpatternsKamal Singh Lodhi Frequent pattern mining aims to discover patterns that occur frequently in a dataset. It finds inherent regularities without any preconceptions. The frequent patterns can then be used for applications like association rule mining, classification, clustering, and more. Two major approaches for mining frequent patterns are the Apriori algorithm and FP-Growth. Apriori is a generate-and-test method while FP-Growth avoids candidate generation by building a compact data structure called an FP-tree to extract patterns directly.
Cs501 intro
Cs501 introKamal Singh Lodhi This document provides information about the CS501 Database Systems and Data Mining course. It includes details about the course structure, timings, syllabus, evaluation policy, and introductory concepts about databases and database management systems. The syllabus covers topics such as data models, query languages, database design, data storage and indexing, query processing, and data mining concepts and techniques. Required textbooks and the evaluation criteria consisting of assignments, quizzes, mid-semester and end-semester exams are also specified.
Cs501 fd nf
Cs501 fd nfKamal Singh Lodhi This document discusses database normalization and functional dependencies. It provides examples of 1st, 2nd, and 3rd normal forms. It defines key concepts like functional dependencies, candidate keys, closure of attribute sets, minimal covers, and extraneous attributes. An example of a supplier-parts database is used to illustrate 2nd normal form. Functional dependencies indicate that city and status are not fully functionally dependent on the primary key, so the relation is not in 2nd normal form.
Cs501 dm intro
Cs501 dm introKamal Singh Lodhi The document provides an introduction to data mining. It discusses the growth of data from terabytes to petabytes and how data mining can help extract knowledge from large datasets. The document outlines the evolution of sciences from empirical to theoretical to computational and now data-driven. It also describes the evolution of database technology and defines data mining as the process of discovering interesting patterns from large amounts of data. The key steps of the knowledge discovery process are discussed.
Cs501 data preprocessingdw
Cs501 data preprocessingdwKamal Singh Lodhi This document discusses data preprocessing and data warehouses. It explains that real-world data is often dirty, incomplete, noisy, and inconsistent. Data preprocessing aims to clean and transform raw data into a format suitable for data mining. The key tasks of data preprocessing include data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques like handling missing data, identifying outliers, and resolving inconsistencies. Data integration combines data from multiple sources. The document also defines characteristics of a data warehouse such as being subject-oriented, integrated, time-variant, and nonvolatile.
Cs501 concurrency
Cs501 concurrencyKamal Singh Lodhi This document discusses concurrency control and recovery techniques for databases. It covers various notions of serializability and recoverability. It describes lock-based protocols like two-phase locking and graph-based protocols like tree protocols. It discusses issues like deadlocks, cascading rollbacks, and starvation. It also covers deadlock handling techniques like prevention, detection and recovery.
Cs501 cluster analysis
Cs501 cluster analysisKamal Singh Lodhi This document provides an overview of cluster analysis techniques. It begins by defining cluster analysis and its applications. It then categorizes major clustering methods into partitioning methods (like k-means and k-medoids), hierarchical methods, density-based methods, grid-based methods, and model-based methods. The document discusses different data types that can be clustered and measures for determining cluster quality. It also outlines requirements for effective clustering in data mining.
Cs501 classification prediction
Cs501 classification predictionKamal Singh Lodhi This document discusses classification and prediction in machine learning. It defines classification as predicting categorical class labels, while prediction models continuous values. The key steps of classification are constructing a model from a training set and using the model to classify new data. Decision trees and rule-based classifiers are described as common classification methods. Attribute selection measures like information gain and gini index are explained for decision tree induction. The document also covers issues in data preparation and model evaluation for classification tasks.
Real Time ImageVideo Processing with Applications in Face Recognition 
Real Time ImageVideo Processing with Applications in Face Recognition Kamal Singh Lodhi This document presents a method for real-time face recognition using principal component analysis (PCA). PCA is used to reduce the dimensionality of face images and encode the key variations between known face images. Faces are represented as vectors, and PCA finds the principal components that best account for variance in the training face data. Test faces are then classified by projecting into this reduced PCA subspace and finding the closest face images. The method achieved a 75% accuracy rate on a test set of 8 faces.
Flow diagram
Flow diagramKamal Singh Lodhi This document outlines steps for uploading data and applying a hybrid discrete wavelet transform and shearlet transform decomposition method for time and frequency domain analysis of signals. The steps include uploading data, applying the hybrid DWT and S-transform for decomposition, correcting any shifts, performing Fourier transform analysis in the time and frequency domains, and measuring the execution time.
860556374-10280271.pptx PETROLEUM COKE CALCINATION PLANT
860556374-10280271.pptx PETROLEUM COKE CALCINATION PLANTPierre Celestin Eyock Project description of petroleum coke calcination plant
AI Chatbots & Software Development Teams
AI Chatbots & Software Development TeamsJoe Krall Welcome to MIND UP: a special presentation for Cloudvirga, a Stewart Title company. In this session, we’ll explore how you can “mind up” and unlock your potential by using generative AI chatbot tools at work.
Curious about the rise of AI chatbots? Unsure how to use them-or how to use them safely and effectively in your workplace? You’re not alone. This presentation will walk you through the practical benefits of generative AI chatbots, highlight best practices for safe and responsible use, and show how these tools can help boost your productivity, streamline tasks, and enhance your workday.
Whether you’re new to AI or looking to take your skills to the next level, you’ll find actionable insights to help you and your team make the most of these powerful tools-while keeping security, compliance, and employee well-being front and center.
Automatic Quality Assessment for Speech and Beyond
Automatic Quality Assessment for Speech and BeyondNU_I_TODALAB
Mila Conversational AI reading group
Wen-Chin Huang：Automatic Quality Assessment for Speech and Beyond，May 2025
名古屋大学 情報学研究科 知能システム学専攻 戸田研究室
VISHAL KUMAR SINGH Latest Resume with updated details
VISHAL KUMAR SINGH Latest Resume with updated detailsVishal Kumar Singh VISHAL KUMAR SINGH Latest Resume with updated details
Machine foundation notes for civil engineering students
Machine foundation notes for civil engineering studentsDYPCET Machine foundation short notes for engineering students
Frontend Architecture Diagram/Guide For Frontend Engineers
Frontend Architecture Diagram/Guide For Frontend EngineersMichael Hertzberg Frontend Architecture chart I made using XMind. No AI help here <3.
DeFAIMint | 🤖Mint to DeFAI. Vibe Trading as NFT
DeFAIMint | 🤖Mint to DeFAI. Vibe Trading as NFTKyohei Ito DeFAI Mint: Vive Trading as NFT.
Welcome to the future of crypto investing — radically simplified.
"DeFAI Mint" is a new frontier in the intersection of DeFi and AI.
At its core lies a simple idea: what if _minting one NFT_ could replace everything else? No tokens to pick.
No dashboards to manage. No wallets to configure.
Just one action — mint — and your belief becomes an AI-powered investing agent.
---
In a market where over 140,000 tokens launch daily, and only experts can keep up with the volatility.
DeFAI Mint offers a new paradigm: "Vibe Trading".
You don’t need technical knowledge.
You don’t need strategy.
You just need conviction.
Each DeFAI NFT carries a belief — political, philosophical, or protocol-based.
When you mint, your NFT becomes a fully autonomous AI agent:
- It owns its own wallet
- It signs and sends transactions
- It trades across chains, aligned with your chosen thesis
This is "belief-driven automation". Built to be safe. Built to be effortless.
- Your trade budget is fixed at mint
- Every NFT wallet is isolated — no exposure beyond your mint
- Login with Twitter — no crypto wallet needed
- No \$SOL required — minting is seamless
- Fully autonomous, fully on-chain execution
---
Under the hood, DeFAI Mint runs on "Solana’s native execution layer", not just as an app — but as a system-level innovation:
- "Metaplex Execute" empowers NFTs to act as wallets
- "Solana Agent Kit v2" turns them into full-spectrum actors
- Data and strategies are stored on distributed storage (Walrus)
Other chains can try to replicate this.
Only Solana makes it _natural_.
That’s why DeFAI Mint isn’t portable — it’s Solana-native by design.
---
Our Vision?
To flatten the playing field.
To transform DeFi × AI from privilege to public good.
To onboard 10,000× more users and unlock 10,000× more activity — starting with a single mint.
"DeFAI Mint" is where philosophy meets finance.
Where belief becomes strategy.
Where conviction becomes capital.
Mint once. Let it invest. Live your life.
PYTHON--QUIZ-1_20250422_002514_0000.pptx
PYTHON--QUIZ-1_20250422_002514_0000.pptxrmvigram Test your knowledge of the Python programming language with this quiz! Covering topics such as:
- Syntax and basics
- Data structures (lists, tuples, dictionaries, etc.)
- Control structures (if-else, loops, etc.)
- Functions and modules
- Object-Oriented Programming (OOP) concepts
Challenge yourself and see how well you can score!
22PCOAM16 ML Unit 3 Full notes PDF & QB.pdf
22PCOAM16 ML Unit 3 Full notes PDF & QB.pdfGuru Nanak Technical Institutions 22PCOAM16 ML Unit 3 notes PDF & QB. pdf
Python Functions, Modules and Packages
Python Functions, Modules and PackagesDr. A. B. Shinde The detailed study on Python Functions, modules and Packages are discussed here
Construction Materials (Paints) in Civil Engineering
Construction Materials (Paints) in Civil EngineeringLavish Kashyap This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
introduction to Rapid Tooling and Additive Manufacturing Applications
introduction to Rapid Tooling and Additive Manufacturing Applicationsvijimech408 In this presentation you will learn about Rapid Tooling and # Additive Manufacturing Applications
A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Mul...
A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Mul...Journal of Soft Computing in Civil Engineering Jamuna river is a morphologically very dynamic river. It carries a vast sediment load from the erosive foothills of Himalaya mountain. The length of the Jamuna River is 220 km. For this research work Jamalpur district is selected to assess morphological changes using hydrodynamic, Artificial intelligence and google satellite images. First, the hydrodynamic model was calibrated and validated at Kazipur station for the years 2018 and 2019 respectively. Then, left overbank maximum discharge, water level, velocity, the slope was extracted from HEC-RAS 1D at 300 m interval interpolated cross-section. Then, this cross-section was exported as a shapefile. In google earth, the erosion rate was measured corresponding to this interpolated cross-section. The results of the hydrodynamic model were given as input variable and erosion rate as an output variable in Machine learning and deep learning technique. Calibration and validation of the regression model was done for the years 2018 and 2019 respectively. This research work can be helpful to locate the area which are vulnerable to bank erosion.
Ad
More Related Content
More from Kamal Singh Lodhi (8)
Cs501 fd nf
Cs501 fd nfKamal Singh Lodhi This document discusses database normalization and functional dependencies. It provides examples of 1st, 2nd, and 3rd normal forms. It defines key concepts like functional dependencies, candidate keys, closure of attribute sets, minimal covers, and extraneous attributes. An example of a supplier-parts database is used to illustrate 2nd normal form. Functional dependencies indicate that city and status are not fully functionally dependent on the primary key, so the relation is not in 2nd normal form.
Cs501 dm intro
Cs501 dm introKamal Singh Lodhi The document provides an introduction to data mining. It discusses the growth of data from terabytes to petabytes and how data mining can help extract knowledge from large datasets. The document outlines the evolution of sciences from empirical to theoretical to computational and now data-driven. It also describes the evolution of database technology and defines data mining as the process of discovering interesting patterns from large amounts of data. The key steps of the knowledge discovery process are discussed.
Cs501 data preprocessingdw
Cs501 data preprocessingdwKamal Singh Lodhi This document discusses data preprocessing and data warehouses. It explains that real-world data is often dirty, incomplete, noisy, and inconsistent. Data preprocessing aims to clean and transform raw data into a format suitable for data mining. The key tasks of data preprocessing include data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques like handling missing data, identifying outliers, and resolving inconsistencies. Data integration combines data from multiple sources. The document also defines characteristics of a data warehouse such as being subject-oriented, integrated, time-variant, and nonvolatile.
Cs501 concurrency
Cs501 concurrencyKamal Singh Lodhi This document discusses concurrency control and recovery techniques for databases. It covers various notions of serializability and recoverability. It describes lock-based protocols like two-phase locking and graph-based protocols like tree protocols. It discusses issues like deadlocks, cascading rollbacks, and starvation. It also covers deadlock handling techniques like prevention, detection and recovery.
Cs501 cluster analysis
Cs501 cluster analysisKamal Singh Lodhi This document provides an overview of cluster analysis techniques. It begins by defining cluster analysis and its applications. It then categorizes major clustering methods into partitioning methods (like k-means and k-medoids), hierarchical methods, density-based methods, grid-based methods, and model-based methods. The document discusses different data types that can be clustered and measures for determining cluster quality. It also outlines requirements for effective clustering in data mining.
Cs501 classification prediction
Cs501 classification predictionKamal Singh Lodhi This document discusses classification and prediction in machine learning. It defines classification as predicting categorical class labels, while prediction models continuous values. The key steps of classification are constructing a model from a training set and using the model to classify new data. Decision trees and rule-based classifiers are described as common classification methods. Attribute selection measures like information gain and gini index are explained for decision tree induction. The document also covers issues in data preparation and model evaluation for classification tasks.
Real Time ImageVideo Processing with Applications in Face Recognition
Real Time ImageVideo Processing with Applications in Face Recognition Kamal Singh Lodhi This document presents a method for real-time face recognition using principal component analysis (PCA). PCA is used to reduce the dimensionality of face images and encode the key variations between known face images. Faces are represented as vectors, and PCA finds the principal components that best account for variance in the training face data. Test faces are then classified by projecting into this reduced PCA subspace and finding the closest face images. The method achieved a 75% accuracy rate on a test set of 8 faces.
Flow diagram
Flow diagramKamal Singh Lodhi This document outlines steps for uploading data and applying a hybrid discrete wavelet transform and shearlet transform decomposition method for time and frequency domain analysis of signals. The steps include uploading data, applying the hybrid DWT and S-transform for decomposition, correcting any shifts, performing Fourier transform analysis in the time and frequency domains, and measuring the execution time.
Recently uploaded (20)
860556374-10280271.pptx PETROLEUM COKE CALCINATION PLANT
860556374-10280271.pptx PETROLEUM COKE CALCINATION PLANTPierre Celestin Eyock Project description of petroleum coke calcination plant
AI Chatbots & Software Development Teams
AI Chatbots & Software Development TeamsJoe Krall Welcome to MIND UP: a special presentation for Cloudvirga, a Stewart Title company. In this session, we’ll explore how you can “mind up” and unlock your potential by using generative AI chatbot tools at work.
Curious about the rise of AI chatbots? Unsure how to use them-or how to use them safely and effectively in your workplace? You’re not alone. This presentation will walk you through the practical benefits of generative AI chatbots, highlight best practices for safe and responsible use, and show how these tools can help boost your productivity, streamline tasks, and enhance your workday.
Whether you’re new to AI or looking to take your skills to the next level, you’ll find actionable insights to help you and your team make the most of these powerful tools-while keeping security, compliance, and employee well-being front and center.
Automatic Quality Assessment for Speech and Beyond
Automatic Quality Assessment for Speech and BeyondNU_I_TODALAB
Mila Conversational AI reading group
Wen-Chin Huang：Automatic Quality Assessment for Speech and Beyond，May 2025
名古屋大学 情報学研究科 知能システム学専攻 戸田研究室
VISHAL KUMAR SINGH Latest Resume with updated details
VISHAL KUMAR SINGH Latest Resume with updated detailsVishal Kumar Singh VISHAL KUMAR SINGH Latest Resume with updated details
Machine foundation notes for civil engineering students
Machine foundation notes for civil engineering studentsDYPCET Machine foundation short notes for engineering students
Frontend Architecture Diagram/Guide For Frontend Engineers
Frontend Architecture Diagram/Guide For Frontend EngineersMichael Hertzberg Frontend Architecture chart I made using XMind. No AI help here <3.
DeFAIMint | 🤖Mint to DeFAI. Vibe Trading as NFT
DeFAIMint | 🤖Mint to DeFAI. Vibe Trading as NFTKyohei Ito DeFAI Mint: Vive Trading as NFT.
Welcome to the future of crypto investing — radically simplified.
"DeFAI Mint" is a new frontier in the intersection of DeFi and AI.
At its core lies a simple idea: what if _minting one NFT_ could replace everything else? No tokens to pick.
No dashboards to manage. No wallets to configure.
Just one action — mint — and your belief becomes an AI-powered investing agent.
---
In a market where over 140,000 tokens launch daily, and only experts can keep up with the volatility.
DeFAI Mint offers a new paradigm: "Vibe Trading".
You don’t need technical knowledge.
You don’t need strategy.
You just need conviction.
Each DeFAI NFT carries a belief — political, philosophical, or protocol-based.
When you mint, your NFT becomes a fully autonomous AI agent:
- It owns its own wallet
- It signs and sends transactions
- It trades across chains, aligned with your chosen thesis
This is "belief-driven automation". Built to be safe. Built to be effortless.
- Your trade budget is fixed at mint
- Every NFT wallet is isolated — no exposure beyond your mint
- Login with Twitter — no crypto wallet needed
- No \$SOL required — minting is seamless
- Fully autonomous, fully on-chain execution
---
Under the hood, DeFAI Mint runs on "Solana’s native execution layer", not just as an app — but as a system-level innovation:
- "Metaplex Execute" empowers NFTs to act as wallets
- "Solana Agent Kit v2" turns them into full-spectrum actors
- Data and strategies are stored on distributed storage (Walrus)
Other chains can try to replicate this.
Only Solana makes it _natural_.
That’s why DeFAI Mint isn’t portable — it’s Solana-native by design.
---
Our Vision?
To flatten the playing field.
To transform DeFi × AI from privilege to public good.
To onboard 10,000× more users and unlock 10,000× more activity — starting with a single mint.
"DeFAI Mint" is where philosophy meets finance.
Where belief becomes strategy.
Where conviction becomes capital.
Mint once. Let it invest. Live your life.
PYTHON--QUIZ-1_20250422_002514_0000.pptx
PYTHON--QUIZ-1_20250422_002514_0000.pptxrmvigram Test your knowledge of the Python programming language with this quiz! Covering topics such as:
- Syntax and basics
- Data structures (lists, tuples, dictionaries, etc.)
- Control structures (if-else, loops, etc.)
- Functions and modules
- Object-Oriented Programming (OOP) concepts
Challenge yourself and see how well you can score!
22PCOAM16 ML Unit 3 Full notes PDF & QB.pdf
22PCOAM16 ML Unit 3 Full notes PDF & QB.pdfGuru Nanak Technical Institutions 22PCOAM16 ML Unit 3 notes PDF & QB. pdf
Python Functions, Modules and Packages
Python Functions, Modules and PackagesDr. A. B. Shinde The detailed study on Python Functions, modules and Packages are discussed here
Construction Materials (Paints) in Civil Engineering
Construction Materials (Paints) in Civil EngineeringLavish Kashyap This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
introduction to Rapid Tooling and Additive Manufacturing Applications
introduction to Rapid Tooling and Additive Manufacturing Applicationsvijimech408 In this presentation you will learn about Rapid Tooling and # Additive Manufacturing Applications
A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Mul...
A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Mul...Journal of Soft Computing in Civil Engineering Jamuna river is a morphologically very dynamic river. It carries a vast sediment load from the erosive foothills of Himalaya mountain. The length of the Jamuna River is 220 km. For this research work Jamalpur district is selected to assess morphological changes using hydrodynamic, Artificial intelligence and google satellite images. First, the hydrodynamic model was calibrated and validated at Kazipur station for the years 2018 and 2019 respectively. Then, left overbank maximum discharge, water level, velocity, the slope was extracted from HEC-RAS 1D at 300 m interval interpolated cross-section. Then, this cross-section was exported as a shapefile. In google earth, the erosion rate was measured corresponding to this interpolated cross-section. The results of the hydrodynamic model were given as input variable and erosion rate as an output variable in Machine learning and deep learning technique. Calibration and validation of the regression model was done for the years 2018 and 2019 respectively. This research work can be helpful to locate the area which are vulnerable to bank erosion.
May 2025 - Top 10 Read Articles in Network Security and Its Applications
May 2025 - Top 10 Read Articles in Network Security and Its ApplicationsIJNSA Journal The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
Transport modelling at SBB, presentation at EPFL in 2025
Transport modelling at SBB, presentation at EPFL in 2025Antonin Danalet Lecture given at EPFL as part of the course "Introduction to transportation systems" in 2025 (Bachelor semester 4, Civil engineering).
Introduction to Additive Manufacturing(3D printing)
Introduction to Additive Manufacturing(3D printing)vijimech408 In this presentation you will learn steps involved in 3D printing
A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Mul...
A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Mul...Journal of Soft Computing in Civil Engineering
Ad
Attribute Classification
- 1. Female Male Three Person Helmet Mask Carrying Bag Two Person Attributes Classification