Wajdi Khattel presented a proposal for a terrorist detection model in social networks. The model uses a multi-dimensional network as input and consists of three sub-models: a text classification model, image classification model, and general information classification model. The sub-models each output a score that is then used by a decision making module to classify a user as a terrorist or not based on a threshold. The implementation involved collecting offline training data from banned Twitter accounts, Google images, and a public dataset. Online data was also collected from Facebook, Instagram, and Twitter using their APIs. Several machine learning models were tested for each sub-model and the proposed full model uses a neural network for text, CNN with data augmentation and
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources on the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies recommender systems that use Linked Data as a source for generating recommendations exploiting the big amount of available resources and the relationships between them. Accordingly, a framework named \emph{AlLied} to execute recommendation algorithms is proposed. This framework can be used as the main component for recommendations in a given architecture because it allows application developers to execute and evaluate recommendation algorithms in different contexts. Two implementations of this framework are presented and compared. The first one relies on graph-based algorithms and the second one on machine learning algorithms. Finally, a new recommendation algorithm that adapts dynamically to the linking features of the datasets used is also proposed
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
A technical seminar delivered on Machine learning in cybersecurity. Machine learning is trending and desired subject this presentation demonstrates how machine learning can be used to protect IT infrastructure
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Read Between The Lines: an Annotation Tool for Multimodal DataDaniele Di Mitri
This is the presentation of Read Between The Lines, the paper which we published at the Learning Analytics & Knowledge Conference 2019 in Tempe, Arizona (#LAK19).
Link to the paper available in Open Access ACM library https://dl.acm.org/citation.cfm?id=3303776
Abstract:
This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.
Flipping 419 Cybercrime Scams: Targeting the Weak and the VulnerableJeremiah Onaolapo
Authors: Gibson Mba, Jeremiah Onaolapo, Gianluca Stringhini, and Lorenzo Cavallaro. Presented at the 26th International World Wide Web Conference/ CyberSafety2017 Workshop, Perth, Australia. Full paper available at http://www0.cs.ucl.ac.uk/staff/J.Onaolapo/papers/wwwcybersafety2017scam.pdf
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources on the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies recommender systems that use Linked Data as a source for generating recommendations exploiting the big amount of available resources and the relationships between them. Accordingly, a framework named \emph{AlLied} to execute recommendation algorithms is proposed. This framework can be used as the main component for recommendations in a given architecture because it allows application developers to execute and evaluate recommendation algorithms in different contexts. Two implementations of this framework are presented and compared. The first one relies on graph-based algorithms and the second one on machine learning algorithms. Finally, a new recommendation algorithm that adapts dynamically to the linking features of the datasets used is also proposed
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
A technical seminar delivered on Machine learning in cybersecurity. Machine learning is trending and desired subject this presentation demonstrates how machine learning can be used to protect IT infrastructure
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Read Between The Lines: an Annotation Tool for Multimodal DataDaniele Di Mitri
This is the presentation of Read Between The Lines, the paper which we published at the Learning Analytics & Knowledge Conference 2019 in Tempe, Arizona (#LAK19).
Link to the paper available in Open Access ACM library https://dl.acm.org/citation.cfm?id=3303776
Abstract:
This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.
Flipping 419 Cybercrime Scams: Targeting the Weak and the VulnerableJeremiah Onaolapo
Authors: Gibson Mba, Jeremiah Onaolapo, Gianluca Stringhini, and Lorenzo Cavallaro. Presented at the 26th International World Wide Web Conference/ CyberSafety2017 Workshop, Perth, Australia. Full paper available at http://www0.cs.ucl.ac.uk/staff/J.Onaolapo/papers/wwwcybersafety2017scam.pdf
Big data cloud-based recommendation system using NLP techniques with machine ...TELKOMNIKA JOURNAL
Recommendation systems (RS) are crucial for social networking sites. Without it, finding precise products is harder. However, existing systems lack adequate efficiency, especially with big data. This paper presents a prototype cloud-based recommendation system for processing big data. The proposed work is implemented by utilizing the matrix factorization method with three approaches. In the first approach, singular value decomposition (SVD) is used, which is an old and traditional recommendation technique. The second recommendation approach is fine-tuned using the alternating least squares (ALS) algorithm with Apache Spark. Finally, the deep neural network (DNN) algorithm is utilized with TensorFlow. This study solves the challenge of handling large-scale datasets in the collaborative filtering (CF) technique after tuning the algorithms by adjusting the parameters in the second approach, which uses machine learning, as well as in the third approach, which uses deep learning. Furthermore, the results of these two approaches outperformed conventional techniques and achieved an acceptable computational time. The dataset size is about 1.5 GB and it is collected from the Goodreads website API. Moreover, the Hadoop distributed file system (HDFS) is used as cloud storage instead of the computer’s local disk for handling larger dataset sizes in the future.
A Customisable Pipeline for Continuously Harvesting Socially-Minded Twitter U...Paolo Missier
talk for paper published at ICWE2019:
Primo F, Missier P, Romanovsky A, Mickael F, Cacho N. A customisable pipeline for continuously harvesting socially-minded Twitter users. In: Procs. ICWE’19. Daedjeon, Korea; 2019.
Robust Expert Finding in Web-Based Community Information SystemsRalf Klamma
Robust Expert Finding in Web-Based Community Information Systems
Ralf Klamma
Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany
A MACHINE LEARNING ENSEMBLE MODEL FOR THE DETECTION OF CYBERBULLYINGijaia
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Talk at the Royal Society Privacy in Statistical Analysis Workshop at Imperial College -- May 3, 2017
http://wwwf.imperial.ac.uk/~nadams/events/ic-rss2017/ic-rss2017.html
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Cryptography and Network Security is a difficult subject to understand, mainly because of the complexity of security protocols and the mathematical rigour required to understand encryption algorithms. Realizing the need for an interactive visualization tool to facilitate the understanding of cryptographic concepts and protocols, several tools had been developed. However, these tools cannot be easily adapted to animate different protocols. The aim of this paper is to propose an interactive visualization tool, called the Cryptographic Protocol Animator (CPAnim). The tool enables a student to specify a protocol and gain knowledge about the impact of its behavior. The protocol is specified by using a scenario-based approach and it is demonstrated as a number of scenes displaying a complete scenario. The effectiveness of this tool was tested using an empirical evaluation method. The results show that this tool was effective in meeting its learning objectives.
AN INTERACTIVE VISUALIZATION TOOL FOR ANIMATING BEHAVIOR OF CRYPTOGRAPHIC PRO...IJNSA Journal
Cryptography and Network Security is a difficult subject to understand, mainly because of the complexity of security protocols and the mathematical rigour required to understand encryption algorithms. Realizing the need for an interactive visualization tool to facilitate the understanding of cryptographic concepts and protocols, several tools had been developed. However, these tools cannot be easily adapted to animate different protocols. The aim of this paper is to propose an interactive visualization tool, called the Cryptographic Protocol Animator (CPAnim). The tool enables a student to specify a protocol and gain knowledge about the impact of its behavior. The protocol is specified by using a scenario-based approach and it is demonstrated as a number of scenes displaying a complete scenario. The effectiveness of this tool was tested using an empirical evaluation method. The results show that this tool was effective in meeting its learning objectives.
In the area of network security, the fundamental security principles and security practice skills are both required for students’ understanding. Instructors have to emphasize both; the theoretical part and practices of security. However, this is a challenging task for instructors’
teaching and students’ learning. For this reason, researchers are eager to support the lecture
lessons by using interactive visualization tools. The learning tool CrypTool 2 is one of these tools that mostly cover all of the above. In fact, the evaluations of the effectiveness of the tools in teaching and learning are limited. Therefore, this paper provides an overview of an empirical evaluation for assessing CrypTool 2 tool. The effectiveness of this tool was tested using an empirical evaluation method. The results show that this visualization tool was effective in
meeting its learning objectives.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Governing Equations for Fundamental Aerodynamics_Anderson2010.pdf
Master's Thesis Presentation
1. Proposal of a Terrorist
Detection Model in
Social Networks
Master’s thesis defense
Presented By : Wajdi Khattel on 07.12.2019
2018 / 2019
In front of jury composed of:
● President: Najet AROUS
● Evaluator: Olfa EL MOURALI
● Academic supervisor: Ramzi GUETARI
● Laboratory supervisor: Nour El Houda BEN CHAABENE
4. Context
▰The appearance of social networks created an ease of
communication
▰The usage of social networks differs: Friendly vs harmful
▰Terrorists are one of the most dangerous category
▰The detection of these users is important
4
5. Problematic
▰Terrorists tend to hide their abnormal behavior
▰Normal user could adopt terrorist behavior
▰Socio-cultural definition of a terrorist could change over time
⇒ Time is important
5
6. Objective
▰Propose a terrorist detection model
▻Consider over-time user’s behavior change
▻Consider over-time behavior’s definition change
▰Cover Limitation of existing models
6
9. Anomaly Detection
9
Paper Input
Format
Description Multiple
Social
Networks
Multiple
Input data
types
User’s
Behavior
Change
Behavior
Definition
Change
Lashakry et al.,
2019
Activity Proposal of model for user profile
creation to monitor users
✓ ✓ ✗ ✗
Zamanian et
al.,2019
Activity Proposal of model for user activity
pattern recognition
✗ ✓ ✓ ✗
Bhattacharjee
et al., 2017
Graph Proposal of a probabilistic anomaly
classifier mode
✗ ✗ ✓ ✓
Chen et al.,
2018
Graph Proposal of a user profiling
framework that can be used to
detect anomalous users
✗ ✓ ✗ ✗
11. 11
Terrorism Detection
Alvari et al. (2019)
- Different data
collecting methods
- Textual-content data
features
Chitrakar et al.
(2016)
Kalpakis et al. (2019)
- Advantages of using
Convolutional Neural
Network (CNN)
- Advantages of using
Transfer Learning
Technique
- Advantages of using
multidimensional
networks
- Social Network
Analysis
methodologies
13. ▰Model Input: Multidimensional Network
▰Three sub-models:
▻Text classification model
▻Image classification model
▻General Information classification model
▰Decision Making
13
Proposed Model
15. ▰Input: Textual data
▰Process:
▻Natural Language Processing
▻Word Embedding
▻Machine Learning classification
▰Output: Score
15
Text Classification Model (TCM)
16. ▰Objective: Make the machine able to understand the human
language
▰Process:
▻Morphological Analysis
▻Syntactical Analysis
▻Semantical Analysis
16
TCM: Natural Language Processing
17. ▰Objective: Represent text in a numerical way
while preserving its semantics
▰Process:
▻Term Frequency-Inverse Document Frequency
(TF-IDF)
17
TCM: Word Embedding
18. ▰Input: Image data
▰Process:
▻Use pre-trained convolutional neural network
model
▻Add new convolutional layers
▰Output: Score
19
Image Classification Model (ICM)
20. ▰Input: General Information data
▰Process:
▻If data is non-numerical ⇒ Encode it
▻Machine Learning classification
▰Output: Score
21
General Information Classification Model
21. ▰Input: 3 submodels scores
▰Process:
▻Calculate user score
▻Classify it based on threshold
▰Output: User category (Terrorist or not)
22
Decision Making
TCM ICM GICM
Decision
Making
S1
S1 = Score1 * Weight1
S2 = Score2 * Weight2
S3 = Score3 * Weight3
S2 S3
24. ▰Offline Data: Data used for the model training
▻Textual Data: Tweets from banned Twitter accounts
▻Image Data: Images from google image
▻General Information Data: PIRUS dataset
▰Online Data: Data used for testing and live usage
▻Facebook Graph API
▻Instagram REST API
▻Twitter REST API
25
Data Collection
