This document summarizes an algorithm called ESW-FI that efficiently mines frequent itemsets from data streams using a sliding window model. The algorithm actively maintains potentially frequent itemsets in a compact data structure using only a single pass over the data. This is an improvement over existing algorithms that require multiple scans or maintaining all transaction data within the window. The ESW-FI algorithm guarantees output quality and bounds memory usage while processing streams of continuous, unpredictable data in a timely manner.
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Automated hierarchical classification of scanned documents using convolutiona...IJECEIAES
This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from Pusat Data Teknologi dan Informasi (Technology and Information Data Center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
This document summarizes a research paper that proposes a new adaptive ensemble boosting classifier for handling concept drifting stream data. It introduces an approach that uses adaptive sliding windows and Hoeffding Trees with naive Bayes as the base learner. The results showed that the proposed algorithm worked well in changing environments compared to other ensemble classifiers. It also discussed types of concept drift in streaming data, including noise, blips, abrupt changes, and gradual changes.
Caching on Named Data Network: a Survey and Future Research IJECEIAES
The IP-based system cause inefficient content delivery process. This inefficiency was attempted to be solved with the Content Distribution Network. A replica server is located in a particular location, usually on the edge router that is closest to the user. The user’s request will be served from that replica server. However, caching on Content Distribution Network is inflexible. This system is difficult to support mobility and conditions of dynamic content demand from consumers. We need to shift the paradigm to content-centric. In Named Data Network, data can be placed on the content store on routersthat are closest to the consumer. Caching on Named Data Network must be able to store content dynamically. It should be selectively select content that is eligible to be stored or deleted from the content storage based on certain considerations, e.g. the popularity of content in the local area. This survey paper explains the development of caching techniques on Named Data Network that are classified into main points. The brief explanation of advantages and disadvantages are presented to make it easy to understand. Finally, proposed the open challenge related to the caching mechanism to improve NDN performance.
Text pre-processing of multilingual for sentiment analysis based on social ne...IJECEIAES
Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text preprocessing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.
Evidence Data Preprocessing for Forensic and Legal AnalyticsCSCJournals
The document discusses best practices for preprocessing evidentiary data from legal cases or forensic investigations for use in analytical experiments. It outlines key steps like identifying the analytical aim or problem based on the case scope or investigation protocol, understanding the case data through assessment and exploration of its format, features, quality, and potential issues. Challenges of working with common text-based case data like emails, social media posts are also discussed. The goal is to clean and transform raw data into a suitable format for machine learning or other advanced analytical techniques while maintaining integrity and relevance to the case.
This document discusses separable reversible data hiding using matrix addition for color images. It proposes a scheme where the content owner encrypts the original image, then a data hider embeds additional data using a data hiding key. The receiver can extract the hidden data and recover the original image by separately providing the encryption key and data hiding key. Lossy and lossless compression techniques for images are also discussed. Reversible data hiding allows exact recovery of the original image and extraction of hidden data without errors when the amount of additional data is large.
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Automated hierarchical classification of scanned documents using convolutiona...IJECEIAES
This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from Pusat Data Teknologi dan Informasi (Technology and Information Data Center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
This document summarizes a research paper that proposes a new adaptive ensemble boosting classifier for handling concept drifting stream data. It introduces an approach that uses adaptive sliding windows and Hoeffding Trees with naive Bayes as the base learner. The results showed that the proposed algorithm worked well in changing environments compared to other ensemble classifiers. It also discussed types of concept drift in streaming data, including noise, blips, abrupt changes, and gradual changes.
Caching on Named Data Network: a Survey and Future Research IJECEIAES
The IP-based system cause inefficient content delivery process. This inefficiency was attempted to be solved with the Content Distribution Network. A replica server is located in a particular location, usually on the edge router that is closest to the user. The user’s request will be served from that replica server. However, caching on Content Distribution Network is inflexible. This system is difficult to support mobility and conditions of dynamic content demand from consumers. We need to shift the paradigm to content-centric. In Named Data Network, data can be placed on the content store on routersthat are closest to the consumer. Caching on Named Data Network must be able to store content dynamically. It should be selectively select content that is eligible to be stored or deleted from the content storage based on certain considerations, e.g. the popularity of content in the local area. This survey paper explains the development of caching techniques on Named Data Network that are classified into main points. The brief explanation of advantages and disadvantages are presented to make it easy to understand. Finally, proposed the open challenge related to the caching mechanism to improve NDN performance.
Text pre-processing of multilingual for sentiment analysis based on social ne...IJECEIAES
Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text preprocessing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.
Evidence Data Preprocessing for Forensic and Legal AnalyticsCSCJournals
The document discusses best practices for preprocessing evidentiary data from legal cases or forensic investigations for use in analytical experiments. It outlines key steps like identifying the analytical aim or problem based on the case scope or investigation protocol, understanding the case data through assessment and exploration of its format, features, quality, and potential issues. Challenges of working with common text-based case data like emails, social media posts are also discussed. The goal is to clean and transform raw data into a suitable format for machine learning or other advanced analytical techniques while maintaining integrity and relevance to the case.
This document discusses separable reversible data hiding using matrix addition for color images. It proposes a scheme where the content owner encrypts the original image, then a data hider embeds additional data using a data hiding key. The receiver can extract the hidden data and recover the original image by separately providing the encryption key and data hiding key. Lossy and lossless compression techniques for images are also discussed. Reversible data hiding allows exact recovery of the original image and extraction of hidden data without errors when the amount of additional data is large.
IRJET- A Study of Privacy Preserving Data Mining and TechniquesIRJET Journal
This document summarizes a study on privacy preserving data mining techniques. It begins with an abstract that introduces privacy preserving data mining as a technique for analyzing shared data while preserving data sensitivity and privacy. It then reviews literature on recent privacy preserving data mining techniques, including techniques for vertically partitioned databases using homomorphic encryption. The document proposes a new privacy preserving association rule mining model and technique. It concludes that privacy preserving data mining is an important new technique for situations where different parties need to combine data for analysis while preserving privacy.
Modelling, Conception and Simulation of a Digital Watermarking System based o...sipij
The digital revolution has increased the production and exchange of high-value documents between institutions, businesses and the general public. In order to secure these exchanges, it is essential to
guarantee the authenticity, integrity and ownership of these documents. Digital watermarking is a possible solution to this challenge as it has already been used for copyright protection, source tracking and video authentication. It also provides integrity protection, which is useful for many types of documents (official documents, medical images). In this paper, we propose a new watermarking solution applicable to images and based on the hyperbolic geometry. Our new solution is based on existing work in the field of digital watermarking.
COMPLETE END-TO-END LOW COST SOLUTION TO A 3D SCANNING SYSTEM WITH INTEGRATED...ijcsit
3D reconstruction is a technique used in computer vision which has a wide range of applications in
areas like object recognition, city modelling, virtual reality, physical simulations, video games and
special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required.
Such systems were often very expensive and was only available for industrial or research purpose.
With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design
inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and
processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully automated by building and
integrating a turntable alongside the software. This means the user can perform a full 3D scan only by
a press of a few buttons from our dedicated graphical user interface. Three main steps were followed
to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system
acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and
convert the acquired point cloud data into a watertight mesh of good quality. Third, export the
reconstructed model to a 3D printer to obtain a proper 3D print of the model.
Ensemble of Probabilistic Learning Networks for IoT Edge Intrusion Detection IJCNCJournal
This paper proposes an intelligent and compact machine learning model for IoT intrusion detection using an ensemble of semi-parametric models with Ada boost. The proposed model provides an adequate realtime intrusion detection at an affordable computational complexity suitable for the IoT edge networks. The proposed model is evaluated against other comparable models using the benchmark data on IoT-IDS and shows comparable performance with reduced computations as required.
A study of existing ontologies in the io t domainSof Ouni
The document discusses existing ontologies in the Internet of Things (IoT) domain. It identifies core concepts needed for an IoT ontology by defining competency questions using the 4W1H methodology. These concepts include sensor, platform, testbed, service, location, and context. The document then surveys existing IoT ontologies based on these concepts and how they address areas like sensor discovery, data description, capabilities, extensibility, and data access. It aims to identify gaps in current ontologies to help define a unified standard ontology for the IoT domain.
A Practical Approach To Data Mining Presentationmillerca2
This document provides an overview of data mining, including common uses, tools, and challenges related to system performance, security, privacy, and ethics. It discusses how data mining involves extracting patterns from data using techniques like classification, clustering, and association rule learning. Maintaining privacy and anonymity while aggregating data from multiple sources for analysis poses ethical issues. The document also offers tips for gaining access to data and navigating performance concerns when conducting data mining projects.
Book of abstract volume 8 no 9 ijcsis december 2010Oladokun Sulaiman
The International Journal of Computer Science and Information Security (IJCSIS) is a publication venue for novel research in computer science and information security. This issue from December 2010 contains 5 research papers. The first paper proposes a 128-bit chaotic hash function that uses the logistic map and MD5/SHA-1 hashes. The second paper discusses constructing an ontology for representing human emotions in videos to improve video retrieval. The third paper proposes an intelligent memory controller for H.264 encoders to reduce external memory access. The fourth paper investigates the impact of fragmentation on query performance in distributed databases. The fifth paper examines the effect of guard intervals in a proposed MIMO-OFDM system for wireless communication.
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IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document describes a decentralized cooperative caching algorithm for social wireless networks that uses hints instead of centralized control. The algorithm allows clients to perform cache functions like replacement and lookup in a decentralized way using hints rather than exact information. This reduces overhead compared to more tightly coordinated systems while still providing comparable performance. The algorithm uses hints for block lookup and replacement decisions instead of relying on a centralized manager. Maintaining accurate hints allows the algorithm to perform well while avoiding the latency and load of centralized coordination.
Iaetsd implementation of chaotic algorithm for secure imageIaetsd Iaetsd
This document proposes a system for secure image transcoding using chaotic algorithm encryption. The system encrypts images using a chaotic key-based algorithm (CKBA) before transcoding. It involves applying the discrete cosine transform, CKBA encryption, quantization, and entropy encoding like Huffman coding. A transcoder block then converts the data to a lower bit rate format while maintaining security. At the receiver, the inverse processes are applied to reconstruct the image. The system aims to provide efficient content delivery with end-to-end security for multimedia applications like mobile web browsing.
Performance Analysis of Various Data Mining Techniques on Banknote Authentica...inventionjournals
In this paper, we describe the functionality features for authenticating in Euro banknotes. We applied different data mining algorithms such as KMeans, Naive Bayes, Multilayer Perceptron, Decision trees (J48), and Expectation-Maximization(EM) to classifying banknote authentication dataset. The experiments are conducted in WEKA. The goal of this project is to obtain the higher authentication rate in banknote classification
A new study of dss based on neural network and data miningAttaporn Ninsuwan
This document proposes using neural networks and data mining to support intelligent decision support systems (IDSS). It discusses how neural networks can help with knowledge learning, problem solving abilities, and real-time processing. Data mining can be used for analysis, clustering, and concept description. The paper then presents a framework for an IDSS combining neural networks, data mining, reasoning, and natural language processing. It provides an example application to evaluate using marsh gas instead of oil and natural gas in China.
New Research Articles 2020 June Issue International Journal on Cryptography a...ijcisjournal
International Journal on Cryptography and Information Security ( IJCIS)
ISSN : 1839-8626
https://wireilla.com/ijcis/index.html
New Research Articles 2020 June Issue International Journal on Cryptography and Information Security (IJCIS)
Selective Encryption of Image by Number Maze Technique
Santosh Mutnuru, Sweeti Kumari Sah and S. Y Pavan Kumar, Eastern Michigan University, USA
Towards A Deeper NTRU Analysis: A Multi Modal Analysis
Chuck Easttom1, Anas Ibrahim2, Alexander Chefranov3, Izzat Alsmadi4 and Richard Hansen5, 1Adjunct Georgetown University and University of Dallas, 2&3Eastern Mediterranean University, 4Texas A&M University, 5Capitol Technology University
https://wireilla.com/ijcis/vol10.html
Data Mining With Excel 2007 And SQL Server 2008Mark Tabladillo
Introduction to Excel 2007 Data Mining Plug-In using SQL Server 2008. The presentation starts with definitions and statistical theory (without equations). Then, the audience interactively participates in four demos showing the power and possibilities of the Microsoft Data Mining Algorithms.
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
Data mining is the process of discovering useful patterns from large amounts of data using statistical, mathematical, and artificial intelligence techniques. It involves applying these techniques to extract and identify useful information from large datasets. Data mining draws from multiple disciplines including statistics, pattern recognition, mathematical modeling, information systems, and machine learning. It has various applications in domains such as customer relationship management, banking, retailing, manufacturing, insurance, software, government, travel, and healthcare. The CRISP-DM process provides a standard methodology for data mining projects involving six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
A comparative analysis of data mining tools for performance mapping of wlan dataIAEME Publication
This document compares the performance of different data mining tools for anomaly detection in wireless network data. It analyzes four tools: Weka, SPSS, Tanagra, and Microsoft SQL Server's Business Intelligence Development Studio. The same wireless network log data with 1000 instances and 13 attributes is clustered into 3 groups (normal activities, suspicious activities, anomalous activities) using different unsupervised learning algorithms in each tool. The results from each tool are different due to using different distance measures and clustering algorithms. The paper aims to interpret the results from each tool and determine which provides the most accurate performance mapping for the wireless network data.
The challenges with respect to mining frequent items over data streaming engaging variable window size
and low memory space are addressed in this research paper. To check the varying point of context change
in streaming transaction we have developed a window structure which will be in two levels and supports in
fixing the window size instantly and controls the heterogeneities and assures homogeneities among
transactions added to the window. To minimize the memory utilization, computational cost and improve the
process scalability, this design will allow fixing the coverage or support at window level. Here in this
document, an incremental mining of frequent item-sets from the window and a context variation analysis
approach are being introduced. The complete technology that we are presenting in this document is named
as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWSCVA).
There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this
design we have used window size change to represent the conceptual drift in an information stream. As it
were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The
experiments that we have executed and documented proved that the algorithm that we have designed is
much efficient than that of existing.
1. The document discusses stream data mining and compares classification algorithms. It defines stream data and challenges in mining stream data.
2. It describes sampling techniques and classification algorithms for stream data mining including Naive Bayesian, Hoeffding Tree, VFDT, and CVFDT.
3. The algorithms are experimentally compared in terms of time, memory usage, accuracy, and ability to handle concept drift. VFDT and CVFDT are found to have advantages over Hoeffding Tree in accuracy while maintaining speed, but CVFDT can additionally detect and respond to concept drift.
The document discusses mining frequent items and item sets from data streams using fuzzy approaches. It describes objectives of mining frequent items from datasets in real-time using fuzzy sets and slices. This involves fetching relevant records, analyzing the data, searching for liked items using fuzzy slices, identifying frequently viewed item lists, making recommendations, and evaluating the results. Algorithms used for mining frequent items from data streams in a single or multiple pass are also reviewed.
IRJET- A Study of Privacy Preserving Data Mining and TechniquesIRJET Journal
This document summarizes a study on privacy preserving data mining techniques. It begins with an abstract that introduces privacy preserving data mining as a technique for analyzing shared data while preserving data sensitivity and privacy. It then reviews literature on recent privacy preserving data mining techniques, including techniques for vertically partitioned databases using homomorphic encryption. The document proposes a new privacy preserving association rule mining model and technique. It concludes that privacy preserving data mining is an important new technique for situations where different parties need to combine data for analysis while preserving privacy.
Modelling, Conception and Simulation of a Digital Watermarking System based o...sipij
The digital revolution has increased the production and exchange of high-value documents between institutions, businesses and the general public. In order to secure these exchanges, it is essential to
guarantee the authenticity, integrity and ownership of these documents. Digital watermarking is a possible solution to this challenge as it has already been used for copyright protection, source tracking and video authentication. It also provides integrity protection, which is useful for many types of documents (official documents, medical images). In this paper, we propose a new watermarking solution applicable to images and based on the hyperbolic geometry. Our new solution is based on existing work in the field of digital watermarking.
COMPLETE END-TO-END LOW COST SOLUTION TO A 3D SCANNING SYSTEM WITH INTEGRATED...ijcsit
3D reconstruction is a technique used in computer vision which has a wide range of applications in
areas like object recognition, city modelling, virtual reality, physical simulations, video games and
special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required.
Such systems were often very expensive and was only available for industrial or research purpose.
With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design
inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and
processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully automated by building and
integrating a turntable alongside the software. This means the user can perform a full 3D scan only by
a press of a few buttons from our dedicated graphical user interface. Three main steps were followed
to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system
acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and
convert the acquired point cloud data into a watertight mesh of good quality. Third, export the
reconstructed model to a 3D printer to obtain a proper 3D print of the model.
Ensemble of Probabilistic Learning Networks for IoT Edge Intrusion Detection IJCNCJournal
This paper proposes an intelligent and compact machine learning model for IoT intrusion detection using an ensemble of semi-parametric models with Ada boost. The proposed model provides an adequate realtime intrusion detection at an affordable computational complexity suitable for the IoT edge networks. The proposed model is evaluated against other comparable models using the benchmark data on IoT-IDS and shows comparable performance with reduced computations as required.
A study of existing ontologies in the io t domainSof Ouni
The document discusses existing ontologies in the Internet of Things (IoT) domain. It identifies core concepts needed for an IoT ontology by defining competency questions using the 4W1H methodology. These concepts include sensor, platform, testbed, service, location, and context. The document then surveys existing IoT ontologies based on these concepts and how they address areas like sensor discovery, data description, capabilities, extensibility, and data access. It aims to identify gaps in current ontologies to help define a unified standard ontology for the IoT domain.
A Practical Approach To Data Mining Presentationmillerca2
This document provides an overview of data mining, including common uses, tools, and challenges related to system performance, security, privacy, and ethics. It discusses how data mining involves extracting patterns from data using techniques like classification, clustering, and association rule learning. Maintaining privacy and anonymity while aggregating data from multiple sources for analysis poses ethical issues. The document also offers tips for gaining access to data and navigating performance concerns when conducting data mining projects.
Book of abstract volume 8 no 9 ijcsis december 2010Oladokun Sulaiman
The International Journal of Computer Science and Information Security (IJCSIS) is a publication venue for novel research in computer science and information security. This issue from December 2010 contains 5 research papers. The first paper proposes a 128-bit chaotic hash function that uses the logistic map and MD5/SHA-1 hashes. The second paper discusses constructing an ontology for representing human emotions in videos to improve video retrieval. The third paper proposes an intelligent memory controller for H.264 encoders to reduce external memory access. The fourth paper investigates the impact of fragmentation on query performance in distributed databases. The fifth paper examines the effect of guard intervals in a proposed MIMO-OFDM system for wireless communication.
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IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document describes a decentralized cooperative caching algorithm for social wireless networks that uses hints instead of centralized control. The algorithm allows clients to perform cache functions like replacement and lookup in a decentralized way using hints rather than exact information. This reduces overhead compared to more tightly coordinated systems while still providing comparable performance. The algorithm uses hints for block lookup and replacement decisions instead of relying on a centralized manager. Maintaining accurate hints allows the algorithm to perform well while avoiding the latency and load of centralized coordination.
Iaetsd implementation of chaotic algorithm for secure imageIaetsd Iaetsd
This document proposes a system for secure image transcoding using chaotic algorithm encryption. The system encrypts images using a chaotic key-based algorithm (CKBA) before transcoding. It involves applying the discrete cosine transform, CKBA encryption, quantization, and entropy encoding like Huffman coding. A transcoder block then converts the data to a lower bit rate format while maintaining security. At the receiver, the inverse processes are applied to reconstruct the image. The system aims to provide efficient content delivery with end-to-end security for multimedia applications like mobile web browsing.
Performance Analysis of Various Data Mining Techniques on Banknote Authentica...inventionjournals
In this paper, we describe the functionality features for authenticating in Euro banknotes. We applied different data mining algorithms such as KMeans, Naive Bayes, Multilayer Perceptron, Decision trees (J48), and Expectation-Maximization(EM) to classifying banknote authentication dataset. The experiments are conducted in WEKA. The goal of this project is to obtain the higher authentication rate in banknote classification
A new study of dss based on neural network and data miningAttaporn Ninsuwan
This document proposes using neural networks and data mining to support intelligent decision support systems (IDSS). It discusses how neural networks can help with knowledge learning, problem solving abilities, and real-time processing. Data mining can be used for analysis, clustering, and concept description. The paper then presents a framework for an IDSS combining neural networks, data mining, reasoning, and natural language processing. It provides an example application to evaluate using marsh gas instead of oil and natural gas in China.
New Research Articles 2020 June Issue International Journal on Cryptography a...ijcisjournal
International Journal on Cryptography and Information Security ( IJCIS)
ISSN : 1839-8626
https://wireilla.com/ijcis/index.html
New Research Articles 2020 June Issue International Journal on Cryptography and Information Security (IJCIS)
Selective Encryption of Image by Number Maze Technique
Santosh Mutnuru, Sweeti Kumari Sah and S. Y Pavan Kumar, Eastern Michigan University, USA
Towards A Deeper NTRU Analysis: A Multi Modal Analysis
Chuck Easttom1, Anas Ibrahim2, Alexander Chefranov3, Izzat Alsmadi4 and Richard Hansen5, 1Adjunct Georgetown University and University of Dallas, 2&3Eastern Mediterranean University, 4Texas A&M University, 5Capitol Technology University
https://wireilla.com/ijcis/vol10.html
Data Mining With Excel 2007 And SQL Server 2008Mark Tabladillo
Introduction to Excel 2007 Data Mining Plug-In using SQL Server 2008. The presentation starts with definitions and statistical theory (without equations). Then, the audience interactively participates in four demos showing the power and possibilities of the Microsoft Data Mining Algorithms.
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
Data mining is the process of discovering useful patterns from large amounts of data using statistical, mathematical, and artificial intelligence techniques. It involves applying these techniques to extract and identify useful information from large datasets. Data mining draws from multiple disciplines including statistics, pattern recognition, mathematical modeling, information systems, and machine learning. It has various applications in domains such as customer relationship management, banking, retailing, manufacturing, insurance, software, government, travel, and healthcare. The CRISP-DM process provides a standard methodology for data mining projects involving six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
A comparative analysis of data mining tools for performance mapping of wlan dataIAEME Publication
This document compares the performance of different data mining tools for anomaly detection in wireless network data. It analyzes four tools: Weka, SPSS, Tanagra, and Microsoft SQL Server's Business Intelligence Development Studio. The same wireless network log data with 1000 instances and 13 attributes is clustered into 3 groups (normal activities, suspicious activities, anomalous activities) using different unsupervised learning algorithms in each tool. The results from each tool are different due to using different distance measures and clustering algorithms. The paper aims to interpret the results from each tool and determine which provides the most accurate performance mapping for the wireless network data.
The challenges with respect to mining frequent items over data streaming engaging variable window size
and low memory space are addressed in this research paper. To check the varying point of context change
in streaming transaction we have developed a window structure which will be in two levels and supports in
fixing the window size instantly and controls the heterogeneities and assures homogeneities among
transactions added to the window. To minimize the memory utilization, computational cost and improve the
process scalability, this design will allow fixing the coverage or support at window level. Here in this
document, an incremental mining of frequent item-sets from the window and a context variation analysis
approach are being introduced. The complete technology that we are presenting in this document is named
as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWSCVA).
There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this
design we have used window size change to represent the conceptual drift in an information stream. As it
were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The
experiments that we have executed and documented proved that the algorithm that we have designed is
much efficient than that of existing.
1. The document discusses stream data mining and compares classification algorithms. It defines stream data and challenges in mining stream data.
2. It describes sampling techniques and classification algorithms for stream data mining including Naive Bayesian, Hoeffding Tree, VFDT, and CVFDT.
3. The algorithms are experimentally compared in terms of time, memory usage, accuracy, and ability to handle concept drift. VFDT and CVFDT are found to have advantages over Hoeffding Tree in accuracy while maintaining speed, but CVFDT can additionally detect and respond to concept drift.
The document discusses mining frequent items and item sets from data streams using fuzzy approaches. It describes objectives of mining frequent items from datasets in real-time using fuzzy sets and slices. This involves fetching relevant records, analyzing the data, searching for liked items using fuzzy slices, identifying frequently viewed item lists, making recommendations, and evaluating the results. Algorithms used for mining frequent items from data streams in a single or multiple pass are also reviewed.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes a new approach called CBSW (Chernoff Bound based Sliding Window) for mining frequent itemsets from data streams. CBSW uses concepts from the Chernoff bound to dynamically determine the window size for mining frequent itemsets. It monitors boundary movements in a synopsis data structure to detect changes in the data stream and adjusts the window size accordingly. Experimental results demonstrate the effectiveness of CBSW in mining frequent itemsets from high-speed data streams.
Different Classification Technique for Data mining in Insurance Industry usin...IOSRjournaljce
this paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.
A New Data Stream Mining Algorithm for Interestingness-rich Association RulesVenu Madhav
Frequent itemset mining and association rule generation is
a challenging task in data stream. Even though, various algorithms
have been proposed to solve the issue, it has been found
out that only frequency does not decides the significance
interestingness of the mined itemset and hence the association
rules. This accelerates the algorithms to mine the association
rules based on utility i.e. proficiency of the mined rules. However,
fewer algorithms exist in the literature to deal with the utility
as most of them deals with reducing the complexity in frequent
itemset/association rules mining algorithm. Also, those few
algorithms consider only the overall utility of the association
rules and not the consistency of the rules throughout a defined
number of periods. To solve this issue, in this paper, an enhanced
association rule mining algorithm is proposed. The algorithm
introduces new weightage validation in the conventional
association rule mining algorithms to validate the utility and
its consistency in the mined association rules. The utility is
validated by the integrated calculation of the cost/price efficiency
of the itemsets and its frequency. The consistency validation
is performed at every defined number of windows using the
probability distribution function, assuming that the weights are
normally distributed. Hence, validated and the obtained rules
are frequent and utility efficient and their interestingness are
distributed throughout the entire time period. The algorithm is
implemented and the resultant rules are compared against the
rules that can be obtained from conventional mining algorithms
Mining Maximum Frequent Item Sets Over Data Streams Using Transaction Sliding...ijitcs
As we know that the online mining of streaming data is one of the most important issues in data mining. In
this paper, we proposed an efficient one- .frequent item sets over a transaction-sensitive sliding window),
to mine the set of all frequent item sets in data streams with a transaction-sensitive sliding window. An
effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and
memory needed to slide the windows. The experiments show that the proposed algorithm not only attain
highly accurate mining results, but also the performance significant faster and consume less memory than
existing algorithms for mining frequent item sets over recent data streams. In this paper our theoretical
analysis and experimental studies show that the proposed algorithm is efficient and scalable and perform
better for mining the set of all maximum frequent item sets over the entire history of the data streams.
FREQUENT ITEMSET MINING IN TRANSACTIONAL DATA STREAMS BASED ON QUALITY CONTRO...IJDKP
The document describes a proposed algorithm called RAQ-FIG for mining frequent itemsets from transactional data streams. It operates using a sliding window model composed of basic windows. The algorithm has three phases: 1) initializing the sliding window by filling it with recent transactions from a buffer, 2) generating bit sequences for each basic window and finding frequent itemsets through bitwise operations, and 3) adapting the algorithm's processing based on available memory and quality metrics to ensure efficient resource usage and accurate results. The algorithm aims to account for computational resources and dynamically adjust the processing rate based on available memory while computing recent approximate frequent itemsets with a single pass.
This document provides an overview of stream data mining techniques. It discusses how traditional data mining cannot be directly applied to data streams due to their continuous, rapid nature. The document outlines some essential methodologies for analyzing data streams, including sampling, load shedding, sketching, and data summarization techniques like reservoirs, histograms, and wavelets. It also discusses challenges in applying these techniques to data streams and open problems in the emerging field of stream data mining.
Mining Stream Data using k-Means clustering AlgorithmManishankar Medi
This document discusses using k-means clustering to analyze urban road traffic stream data. Stream data arrives continuously over time and is challenging to process due to its high volume, velocity and volatility. The document proposes using a sliding window technique with k-means clustering to analyze recent urban traffic data and visualize clusters in real-time to provide insights into traffic patterns and congested roads. This analysis could help travelers and authorities respond to traffic issues more quickly.
An Improved Differential Evolution Algorithm for Data Stream ClusteringIJECEIAES
A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.
This document presents an analytical framework for classifying data stream mining techniques based on their approaches to challenges. It discusses how data streams pose computational challenges due to their continuous, massive, and potentially infinite nature. It classifies data stream mining challenges and techniques for addressing them, such as approaches that modify existing data mining algorithms or develop new ones. The document proposes an analytical framework to evaluate how data mining applications can help develop novel data stream mining algorithms to handle different tasks.
An Efficient Algorithm for Mining Frequent Itemsets within Large Windows over...Waqas Tariq
Sliding window is an interesting model for frequent pattern mining over data stream due to handling concept change by considering recent data. In this study, a novel approximate algorithm for frequent itemset mining is proposed which operates in both transactional and time sensitive sliding window model. This algorithm divides the current window into a set of partitions and estimates the support of newly appeared itemsets within the previous partitions of the window. By monitoring essential set of itemsets within incoming data, this algorithm does not waste processing power for itemsets which are not frequent in the current window. Experimental evaluations using both synthetic and real datasets shows the superiority of the proposed algorithm with respect to previously proposed algorithms.
This document outlines a presentation on data mining techniques. It discusses data compression methods like null compression and run length encoding. It also discusses association rule mining and the Apriori algorithm limitations. The problem statement proposes a method for compressing databases that can be decompressed while also improving data mining performance. The proposed work involves compressing data into groups, generating frequent itemsets using Apriori on the compressed data, then decompressing and generating association rules. The implementation environment and conclusions are also outlined. References on related work are provided at the end.
Concept Drift Identification using Classifier Ensemble Approach IJECEIAES
Abstract:-In Internetworking system, the huge amount of data is scattered, generated and processed over the network. The data mining techniques are used to discover the unknown pattern from the underlying data. A traditional classification model is used to classify the data based on past labelled data. However in many current applications, data is increasing in size with fluctuating patterns. Due to this new feature may arrive in the data. It is present in many applications like sensornetwork, banking and telecommunication systems, financial domain, Electricity usage and prices based on its demand and supplyetc .Thus change in data distribution reduces the accuracy of classifying the data. It may discover some patterns as frequent while other patterns tend to disappear and wrongly classify. To mine such data distribution, traditionalclassification techniques may not be suitable as the distribution generating the items can change over time so data from the past may become irrelevant or even false for the current prediction. For handlingsuch varying pattern of data, concept drift mining approach is used to improve the accuracy of classification techniques. In this paper we have proposed ensemble approach for improving the accuracy of classifier. The ensemble classifier is applied on 3 different data sets. We investigated different features for the different chunk of data which is further given to ensemble classifier. We observed the proposed approach improves the accuracy of classifier for different chunks of data.
This document summarizes a survey on data mining. It discusses how data mining helps extract useful business information from large databases and build predictive models. Commonly used data mining techniques are discussed, including artificial neural networks, decision trees, genetic algorithms, and nearest neighbor methods. An ideal data mining architecture is proposed that fully integrates data mining tools with a data warehouse and OLAP server. Examples of profitable data mining applications are provided in industries such as pharmaceuticals, credit cards, transportation, and consumer goods. The document concludes that while data mining is still developing, it has wide applications across domains to leverage knowledge in data warehouses and improve customer relationships.
This document discusses an approach for mining frequent itemsets from data streams using the Chernoff bound and sliding window model. The proposed CB-based method approximates itemset counts from summary information without rescanning the stream, making it adaptive to streams with different distributions. Experiments showed the method performs better in optimizing memory usage and mining recent patterns in less time with accurate results. The document reviews related work on frequent itemset mining from data streams and motivates the need for an efficient model to handle time-sensitive items in uncertain streams.
This document discusses privacy-preserving techniques for data stream mining. It proposes a hybrid method that uses both rotation and translation transformations to perturb data streams and preserve privacy. The key steps are:
1) The data stream is represented as a matrix and only numeric attributes are considered.
2) Attribute pairs are randomly selected and perturbed using rotation transformations within a calculated "security range".
3) Additional attributes are perturbed using translation transformations, where random numbers generated by a secure function determine whether values are added to or subtracted from the original data.
4) The perturbed data stream is then used for clustering and analysis while preserving privacy. The goal is to maximize both privacy and utility of results.
This document discusses privacy-preserving techniques for data stream mining. It proposes a hybrid method that uses both rotation and translation based data perturbation to anonymize sensitive attributes in data streams. The key steps are:
1) Select attribute pairs and set security thresholds for perturbation.
2) Apply rotation transformations to selected attribute pairs to distort the data within the security thresholds.
3) Also apply translation perturbations by adding or subtracting random noise values to other attributes.
The goal is to anonymize the data enough to preserve privacy while maintaining accuracy for data stream mining tasks like clustering. Evaluation focuses on balancing privacy protections with preserving data utility for analysis.
Database techniques for resilient network monitoring and inspectionTELKOMNIKA JOURNAL
Network connection logs have long been recognized as integral to proper network security, maintenance, and performance management. This paper provides a development of distributed systems and write optimized databases: However, even a somewhat sizable network will generate large amounts of logs at very high rates. This paper explains why many storage methods are insufficient for providing real-time analysis on sizable datasets and examines database techniques attempt to address this challenge. We argue that sufficient methods include distributing storage, computation, and write optimized datastructures (WOD). Diventi, a project developed by Sandia National Laboratories, is here used to evaluate the potential of WODs to manage large datasets of network connection logs. It can ingest billions of connection logs at rates over 100,000 events per second while allowing most queries to complete in under one second. Storage and computation distribution are then evaluated using Elastic-search, an open-source distributed search and analytics engine. Then, to provide an example application of these databases, we develop a simple analytic which collects statistical information and classifies IP addresses based upon behavior. Finally, we examine the results of running the proposed analytic in real-time upon broconn (now Zeek) flow data collected by Diventi at IEEE/ACM Supercomputing 2019.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations