Textual Data Compression

Federated Learning (FL) is a machine learning technique that helps safeguard data privacy by financial institutions to collaborate to train AI models without exchanging real data. In FL, the AI model is distributed to each institution, whereas in traditional machine learning, all the data is sent to a single location for training the data, each bank or institution uses its own private financial data to train the model and only transmits model updates to a central server. For activities including identifying fraud, evaluating credit risk, executing automated transacti providing individualized services, and forecasting market trends, this method assists in identifying helpful patterns in financial data. Crucially, it adheres to significant laws and regulations and protects data privacy..Our research focuses on developing a privacy address the unique difficulties of handling data dispersed across several locations, guaranteeing security, and adhering to legal requiremen create intelligent, safe, and cooperative AI systems In today's financial landscape, privacy and security of sensitive data have and increasing cyber threats. Traditional machine learning approaches require pooling data from multiple financial institutions into a centralized system, which raises significant privacy concerns and risks of Learning (FL) emerges as a groundbreaking solution to address these challenges by enabling decentralized, collaborative model training across multiple entities without sharing raw data participating institutions such as banks and fintech companies, where the model is locally trained on private financial data. Instead of sending actual data, only encrypted updates of the model are aggregated centrally to form an improved global model. This privacy accurate and robust AI applications while adhering to legal and ethical standards. Within the financial sector, FL finds critical applications in trading, personalized services, and market trend forecasting. Fraud detection is a particularly vital area where FL enables multiple banks to collaboratively identify fraudulent activities using de reducing false positives. However, deploying FL in finance introduces unique challenges such as dealing with heterogeneous and imbalanced data, ensuring secure communication, and defending against adversar focuses on developing and analyzing a federated learning framework specifically tailored for the finance domain. It covers the architecture of FL systems, privacy and security measures, implementation strategies, and evaluation metr world case studies demonstrate the efficacy and benefits of FL in financial applications. Lastly, the paper explores future directions in federated finance AI, emphasizing explain illustrate how FL can harness financial data responsibly and collaboratively to build intelligent, secure, and privacy AI systems in modern finance.

The "XOR-Torus" Implementation This document outlines the systematic approach to establishing the Blackwell Block, pairing the SHD-CCP Stream, and optimizing the Linguistic Crystallization pipeline for benchmarking on NVIDIA SM100 architectures. The migration to the SM100 architecture is the baseline requirement for silicon-native Linguistic Crystallization. The Blackwell Block serves as the atomic compute unit, engineered to enforce the SHD-CCP (Super-High-Dimensional Codex-Coupled Pre-processing) protocol at the hardware level. This transition provides the hardware-accelerated primitives necessary to process high-dimensional manifolds with deterministic latency, effectively resolving the computational bottlenecks inherent in standard vector-processing architectures.

This study explores the use of sentiment analysis and machine learning models to predict the market trends of meme
coins. By analyzing social media sentiment and financial metrics, the research achieved a 74% accuracy rate in forecasting both
bullish and bearish market movements. Despite these promising results, challenges such as fluctuating sentiment and the quality
of data persist. Future studies should aim to incorporate a broader range of data sources and advanced machine learning
techniques to improve the precision of predictions.

Accurate real estate price prediction is crucial in today's market to aid buyers, sellers, and investors in making informed decisions. This study employs machine learning algorithms-specifically Linear Regression, Decision Tree Regression, and Random Forest Regression-to model and predict housing prices based on various influential features. The methodology involves data preprocessing, feature engineering, and model evaluation using standard metrics like R² score and Root Mean Squared Error (RMSE). The models are trained on real-world housing datasets, and results demonstrate the efficiency of ensemble learning over traditional linear approaches. This paper establishes that Random Forest offers the most accurate predictions and is suitable for practical applications in real estate.

Unification is known to be the most repeated operation in logic programming and PROLOG interpreters. To speed up the execution of logic programs, the performance of unification must be improved. We propose a parallel unification machine for speeding up the unification algorithm. The machine is simulated at the register transfer level and the simulation results as well as performance comparison with a serial unification coprocessor are presented.

The development of a highly resilient architecture for mission-critical systems is an integrated approach aimed at minimizing operational risks and ensuring the continuity of vital services. In the face of growing threats, including natural disasters and man-made disasters, the sustainability of infrastructure is becoming a key aspect of security and stability. The basic principles of development include reliability, flexibility, and environmental friendliness, which allows the system not only to withstand external influences but also to quickly recover from failures including bad updates in support software like Microsoft Windows Operating Systems and Endpoint Detection and Response (EDR) such as CloudStrike. The implementation of such architectures requires risk analysis, diversifying security and/or management tooling, integration of backup systems, regular monitoring, and staff training. The practical implementation of highly resilient architectures in an organization can significantly reduce the likelihood of failures and minimize the consequences of unforeseen situations such as seen by the recent Microsoft Windows and CloudStrike updates that impacted over 8 million computers which appears to be the latest failure of IT infrastructure. This approach ensures both the protection of information and data and the maintenance of the health of critical business processes.

This paper presents an efficient data compression technique based on using Lempel-Ziv coding algorithms such as the LZ-78 algorithm. The conventional LZ-78 algorithm was applied directly to a non-binary information source (i.e., original source) with a large number of alphabets (such as 256 characters in English text). However, this modified technique applies the LZ-78 algorithm to an nth-order extension equivalent binary source which includes 2 n symbols. The equivalent binary source is generated by applying an efficient source encoding scheme on the original one. In this encoding scheme, each alphabet in the original source is given a weighted fixed-length code (e.g. eight bits/character). The weighted codes are chosen in such a way that the entropy of the generated binary source is made as close as possible to that of the original one. The reduction in number of symbols in the extended binary source leads to a better compression algorithm performance which can be measured based o...

Data compression has a paramount effect on Data warehouse for reducing data size and improving query processing. Distinct compression techniques are feasible at different levels, each of types either give good compression ratio or suitable for query processing. This paper focuses on applying lossless and lossy compression techniques on relational databases. The proposed technique is used at attribute level on Data warehouse by applying lossless compression on three types of attributes (string, integer, and float) and lossy compression on image attribute.

The research on the phenomenon of text compression lies in response to the ever-increasing demands of the modern information society. These demands are intricately tied to the efficient utilization of knowledge and the continuous pursuit of enhancing the methods for processing information. Hence, the relevance of this research is derived from the active manifestation of the need to provide students with the skills required for compressing information. The significance of this research stems from the tangible demonstration of the imperative to equip students with the essential aptitude for information compression. The primary aim of this study is to discern the distinct features pertaining to various principles employed in the compression of information. The primary analytical approach employed in this research is comparative and content analyses. The research predominantly relies on the inductive method of analysis, wherein the investigation progresses from specific linguistic observations towards discerning systematic relationships among them. Within the frame of the research, the qualitative survey conducted in 2022-23 at the university of Georgia involved a sample of 50 text constructors (undergraduate and graduate students studying English Philology). The survey encompassed one embedded unit, ensuring gender balance, and the respondents were selected using a randomizing principle. The research material, comprising scientific articles, was provided in English. Upon analyzing the collected data, certain regularities emerged: 1. Abstracts generated by different individuals exhibit subjectivity. 2. The subjectivity of the abstracts generated by individuals is influenced by the gender of the text constructors. The research findings have substantiated the significance of redundancy when examining the phenomenon of text compression.

The research on the phenomenon of text compression lies in response to the ever-increasing demands of the modern information society. These demands are intricately tied to the efficient utilization of knowledge and the continuous pursuit of enhancing the methods for processing information. Hence, the relevance of this research is derived from the active manifestation of the need to provide students with the skills required for compressing information. The significance of this research stems from the tangible demonstration of the imperative to equip students with the essential aptitude for information compression. The primary aim of this study is to discern the distinct features pertaining to various principles employed in the compression of information. The primary analytical approach employed in this research is comparative and content analyses. The research predominantly relies on the inductive method of analysis, wherein the investigation progresses from specific linguistic observations towards discerning systematic relationships among them. Within the frame of the research, the qualitative survey conducted in 2022-23 at the university of Georgia involved a sample of 50 text constructors (undergraduate and graduate students studying English Philology). The survey encompassed one embedded unit, ensuring gender balance, and the respondents were selected using a randomizing principle. The research material, comprising scientific articles, was provided in English. Upon analyzing the collected data, certain regularities emerged: 1. Abstracts generated by different individuals exhibit subjectivity. 2. The subjectivity of the abstracts generated by individuals is influenced by the gender of the text constructors. The research findings have substantiated the significance of redundancy when examining the phenomenon of text compression.

Remote sensor systems maintain numerous applications in various fields. Sparing vitality in such systems is continuously a basic issue that should be considered to delay the network lifespan. Bunching in the systems is additionally considered as a vitality sparing directing strategy. This manuscript, propose the two technique by which the vitality problem of a remote sensor can be resolved. In first Strategy, with the help of wireless power transfer recharge all the node of the network and in the second strategy a new information gathering strategy, called firmness technique, keeping in mind the end goal to altogether diminish information transmitting among grouped systems for vitality sparing reason. In the strategy, Wavelet essential is utilized in each group to pack information to be sent to the base-station (BS). At the BS, all the detecting information can be remade in light of the compacted tests gathered from groups. We additionally investigate what's more, plan the aggregate vitality utilization for information transmitting between sensors in the systems and from the systems to the BS. Reproduction comes about are given to illuminate our examination and to propose ideal cases for the system to expend the slightest control. In this manuscript, new proposed technique shows the results with various performance parameters like, power consumption, network lifespan in Inter and Intra transmissions.

In this current age both communication and generic file compression technologies are using different kind of efficient data compression methods massively. This paper surveys a variety of data compression methods. The aim of data compression is to reduce redundancy in stored or communicated data. Data compression has important application in the area of file storage and distributed system. This paper will provide an overview of several compression methods and will formulate new algorithms that may improve compression ratio and abate error in the reconstructed data. In this work the data compression techniques: Huffman, Run-Length, LZW, Shannon-Fano, Repeated-Huffman, Run-Length-Huffman, and Huffman-Run-Length are tested against different types of multimedia formats such as images and text, which shows the difference of various data compression methods on image and text file.

A representation method using the non-symmetry and anti-packing model (NAM) for data compression of binary images is presented. The NAM representation algorithm is compared with the popular linear quadtree and run length encoding algorithms. Theoretical and experimental results show that the algorithm has a higher compression ratio for both lossy and lossless cases of binary images and better reconstructed quality for the lossy case.

Data compression is an art used to reduce the number of bits required to transmit the data of particular information. The goal of data compression is to eliminate the redundancy in a data in order to reduce its size. Data compression can either be lossless or lossy. Lossless data compression recreates the exact original data from the compressed data while lossy data compression cannot regenerate the perfect original data from the compressed data. The relevance and importance of data compression has shown a growing trend from the past few years and is expected to continue remaining the same in the coming time. The proposed will provide an overview of popular adaptive data compression methods of general utility. It evaluated these existing algorithms with a newly proposed scheme with improvements to make the algorithm fully complete. The size data to be transmitted has an impact on the efficient working of WSN. This research work reports the simulation & implementation of efficient da...

A new notion, that of semi-lossless text compression, is introduced, and its applicability in various settings is investigated. First results suggest that it might be hard to exploit the additional redundancy of English texts, but the new methods could be useful in applications where the correct spelling is not important, such as in short emails, and the new notion raises some interesting research problems in several different areas of Computer Science.

It seems reasonable to expect from a good compression method that its output should not be further compressible, because it should behave essentially like random data. We investigate this premise for a variety of known lossless compression techniques, and find that, surprisingly, there is much variability in the randomness, depending on the chosen method. Arithmetic coding seems to produce perfectly random output, whereas that of Huffman or Ziv-Lempel coding still contains many dependencies. In particular, the output of Huffman coding has already been proven to be random under certain conditions, and we present evidence here that arithmetic coding may produce an output that is identical to that of Huffman.

Huffman coding is known to be optimal, yet its dynamic version may yield smaller compressed files. The best known bound is that the number of bits used by dynamic Huffman coding in order to encode a message of n characters is at most larger by n bits than the number of bits required by static Huffman coding. In particular, dynamic Huffman coding can also generate a larger encoded file than the static variant, though in practice the file might often, but not always, be smaller. We propose here a new dynamic Huffman encoding approach, that provably always performs at least as good as static Huffman coding, and may be better than the standard dynamic Huffman coding for certain files. This is achieved by reversing the direction for the references of the encoded elements to those forming the model of the encoding, from pointing backwards to looking into the future.

With increasing need to store data in lesser memory several lossless compression techniques are developed. This paper intends to provide the performance analysis of lossless compression techniques over various parameters like compression ratio, delay in processing, size of image etc. It provides the relevant data about variations in them as well as to describe the possible causes for it. It describes the basic lossless techniques as Huffman encoding, run length encoding, arithmetic encoding and Lempel-ziv-welch encoding briefly with their effectiveness under varying parameters. Considering the simulation results of grayscale image compression achieved in MATLAB software, it also focused to propose the possible reasons behind differences in comparison.

The development of data storage hardware is very rapidly over time. In line with the development of storage hardware, the amount of digital data shared on the internet is increasing every day. That way no matter how big the size of the storage device we have, of course, it will only be a matter of time until that storage space is exhausted. Therefore, in terms of maximizing storage space, a technique called compression emerged. This study focuses on a  comparative analysis of 2 Lossless compression technique algorithms, namely the Huffman algorithm and Lempel Ziv Welch (LZW). A number of test files with different file types are applied to both algorithms that are compared. The performance of the algorithm is determined based on the comparison of space saving and compression time. The test results showed that the Lempel Ziv Welch (LZW) algorithm was superior to Huffman’s algorithm in .txt file type compression and .csv, the average space savings produced were 63.85% and 77.56%. The d...

The main goal of data compression is to decrease redundancy in warehouse or communicated data, so growing effective data density. It is a common necessary for most of the applications. Data compression is very important relevancy in the area of file storage and distributed system just because of in distributed system data have to send from and to all system. Two configuration of data compression are there "lossy" and "lossless". But in this paper we only focus on Lossless data compression techniques. In lossless data compression, the wholeness of data is preserved. Data compression is a technique that decreases the data size, removing the extreme information. Data compression has many types of techniques that decrease redundancy. The methods which mentioned are Run Length Encoding, Shannon Fanon, Huffman, Arithmetic, adaptive Huffman, LZ77, LZ78 and LZW with its performance.

Data compression is now almost a common requirement for every applications as it is a means for saving the channel bandwidth and storage space. Data Compression is an art of allowing a technique to reduce the volume of data i.e. excess information, by maintaining the quality of data. There a number of algorithms available for compression of files of different formats. But, algorithm is to be such a chosen which reduces redundancy of data by consuming less time and providing more compression ratio as compared to other techniques. So, even for a single data type, numbers of approaches are available and to select among them the best one depending upon the need is very important and a difficult task. Compression methods are categorized as Lossy and Lossless but in this paper focus is only on Lossless text compression techniques. The methods which are discussed are Run Length Encoding, Shannon Fanon, Huffman, Arithmetic, LZ77, LZ78 and LZW with its performance.

The purpose of the study was to compare the compression ratios of file size, file complexity, and time used in compressing each text file in the four selected compression algorithms on a given modern computer running Windows 7. The researcher used the Java programming language on the NetBeans development environment to create user-friendly user interfaces that displayed both the content being compressed and the output generated by the compression. A purposive sampling technique was used to select text files with varying complexities from the Google data store, as well as other text documents developed and manipulated by the researcher to meet the level of complexity required for the research experiment. The results showed that each compression algorithm compressed differently in terms of word count. This is due to the fact that the compression ratios of the number of words in each file changed in each compression algorithm. Furthermore, the complexity of the text in the file had no ...

Huffman coding is known to be optimal, yet its dynamic version may yield smaller compressed files. The best known bound is that the number of bits used by dynamic Huffman coding in order to encode a message of n characters is at most larger by n bits than the size of the file required by static Huffman coding. In particular, dynamic Huffman coding can also generate a larger encoded file than the static variant, though in practice the file might sometimes be smaller. We propose here a new variant of Huffman encoding, that provably always performs better than static Huffman coding by at least m − 1 bits, where m denotes the size of the alphabet, and may be better than the standard dynamic Huffman coding for certain files. The algorithm is based on reversing the direction for the references of the encoded elements, from pointing backwards into the past to looking forward into the future.

As the volume and importance of textual data in data science continues to grow, combined with advancements in its techniques, it has created numerous opportunities for extracting valuable insights from textual information. However, privacy and the security of private data are issues that are brought up by the analysis of text data. This paper presents an in-depth analysis of security measures specifically designed for protecting textual data in data science applications. It explores various techniques and strategies to safeguard the confidentiality, integrity, and availability of textual data throughout its lifecycle. The objective is to provide data scientists and practitioners with a comprehensive understanding of the security challenges and best practices associated with textual data in the context of data science.

In this paper, a energy-efficient data collection method is proposed in which an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be transformed in DCT domain, we propose that each cluster from the networks only sends a small number of large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the large coefficients received at the BS. We further analyze and formulate the communication cost as the power consumption for transmitting data in such networks based on stochastic problems. Some common clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise and noiseless environments for the proposed method are considered.

As the volume and importance of textual data in data science continues to grow, combined with advancements in its techniques, it has created numerous opportunities for extracting valuable insights from textual information. However, privacy and the security of private data are issues that are brought up by the analysis of text data. This paper presents an in-depth analysis of security measures specifically designed for protecting textual data in data science applications. It explores various techniques and strategies to safeguard the confidentiality, integrity, and availability of textual data throughout its lifecycle. The objective is to provide data scientists and practitioners with a comprehensive understanding of the security challenges and best practices associated with textual data in the context of data science.

Video compression is nothing but compression of video, it involves compression of video size, audio format. In other words we can state video compression as one of the encoding format of video that it can have less memory size than the original video file. One of the basic need for video compression is nothing but, if video size less is then it is easy to transmit over the air i.e. on internet. Video compression is done by removing repetitive frames and audio effects so, mainly it will reduce all unimportant data or it will reduce it from video file. There are different algorithms for compression of video. We are going to use LZW compression algorithm to do effective compression of digital video. LZW is dictionary based algorithm. This paper implements one of the advanced video compression technique by providing less data loss by using LZW algorithm. IndexTerms Image Processing, Video Processing, Video Compression, Video Quality, Decompression.

We introduce DashHashLM, an efficient data structure that stores an n-gram language model compactly while making minimal trade-offs on runtime lookup latency. The data structure implements a finite state transducer with a lossless structural compression and outperforms comparable implementations when considering lookup speed in the small-footprint setting. DashHashLM introduces several optimizations to language model compression which are designed to minimize expected memory accesses. We also present variations of Dash-HashLM appropriate for scenarios with different memory and latency constraints. We detail the algorithm and justify our design choices with comparative experiments on a speech recognition task. Specifically, we show that with roughly a 10% increase in memory size, compared to a highly optimized, compressed baseline n-gram representation, our proposed data structure can achieve up to a 6x query speedup.

The optimal configuration for a Large Scale Wireless Sensor Networks (LS-WSN) is the one that minimizes the sampling rate, the CPU time and the channel accesses (thus maximizing the network lifetime), with a controlled distortion in the recovered data. Initial deployments of LS-WSN are usually not able to adapt to changing environments and rarely take into account either the spatial or temporal nature of the sensed variables, both techniques that optimize the network operation. In this work we propose the use of Self-Organized Distributed Compressive Projection (SODCP) in order to let the nodes to form clusters in a distributed and data-driven way, exploiting the spatial correlation of the sensed data. We compare the performance of this innovative technique, using actual data from the LUCE LS-WSN, with two different baselines: Centralized Compressive Projection (CCP) and Distributed Compressive Projection (DCP). The former uses no clustering, whereas the latter makes use of an a priori clustering that favors proximity and balances the number of nodes in each cluster. We show that SODCP outperforms DCP (in terms of Signal-to-Noise vs. Compression Rate). We also show that the performance of SODCP converges to that of CCP for relatively high compression rates of 55%.

Failure of Hard Disk is a term most companies and people, fear about. People get concerned regarding data loss. Therefore, predicting the failure of the HDD is an important and to ensure the storage security of the data center. There exist a system named, S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology) in hard disk tools or bios tools which stands for Self-Monitoring, Analysis and Reporting Technology. Our project will be predicting the failure of hard drive whether it will fail or not. This prediction will be based on Machine Learning algorithm. S.M.A.R.T. values of hard disk will be extracted from external tool.

Noise is an ever present phenomenon while dealing with recording devices, be it digital or analog, be it specks in images or background hiss in music recordings. Therefore, this paper aims at ways of reducing the effects of these forms of noise on image and sound files that can be compressed and sent through a communication channel. This noise reduction mechanism is represented in the form of algorithms and combined with data compression to minimize the effects of noise, while images and sounds are transmitted with their original content undistorted.

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compress...

In this paper, a energy-efficient data collection method is proposed in which an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be transformed in DCT domain, we propose that each cluster from the networks only sends a small number of large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the large coefficients received at the BS. We further analyze and formulate the communication cost as the power consumption for transmitting data in such networks based on stochastic problems. Some common clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise and noiseless environments for the proposed method are considered.

Failure of Hard Disk is a term most companies and people, fear about. People get concerned regarding data loss. Therefore, predicting the failure of the HDD is an important and to ensure the storage security of the data center. There exist a system named, S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology) in hard disk tools or bios tools which stands for Self-Monitoring, Analysis and Reporting Technology. Our project will be predicting the failure of hard drive whether it will fail or not. This prediction will be based on Machine Learning algorithm. S.M.A.R.T. values of hard disk will be extracted from external tool.

In this paper, a energy-efficient data collection method is proposed in which an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be transformed in DCT domain, we propose that each cluster from the networks only sends a small number of large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the large coefficients received at the BS. We further analyze and formulate the communication cost as the power consumption for transmitting data in such networks based on stochastic problems. Some common clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise and noiseless environments for the proposed method are considered.

Obiectivul utilitarului ICompress, descris în acest articol, este studiul în condiţii reale al compresiei secvenţelor masive de date având un număr limitat de culori. Datorită faptului că standardele actuale nu oferă suficientă flexibilitate, precum şi a implementării unor rutine de compresie şi decompresie pentru aceste standarde, care nu diferă foarte mult faţă de aplicaţiile existente, articolul propune crearea unor noi formate de fişiere în scopul implementării compresiei fără pierderi. Algoritmii de compresie studiaţi sunt LZW şi RLC, pentru fiecare în parte introducându-se un format propriu şi studiindu-se performanţele în cazul unor imagini medicale.

Video compression is nothing but compression of video, it involves compression of video size, audio format. In other words we can state video compression as one of the encoding format of video that it can have less memory size than the original video file. One of the basic need for video compression is nothing but, if video size less is then it is easy to transmit over the air i.e. on internet. Video compression is done by removing repetitive frames and audio effects so, mainly it will reduce all unimportant data or it will reduce it from video file. There are different algorithms for compression of video. We are going to use LZW compression algorithm to do effective compression of digital video. LZW is dictionary based algorithm. This paper implements one of the advanced video compression technique by providing less data loss by using LZW algorithm. IndexTerms Image Processing, Video Processing, Video Compression, Video Quality, Decompression.

Autonomous driving is gaining its importance due to the advancements in technology. With the intention of safety during human driving and with the longer-term aim to act as a communication enabler for autonomous driving, vehicle to vehicle communication is gaining its importance. In this paper, we discuss and compare various source coding techniques that can be used for vehicle to vehicle communication. We propose abbreviation-based and probability-based source coding methods for the vehicle to vehicle communication. We compare the proposed application-specific source coding methods with other techniques like Huffman, Arithmetic, and Lempel-Ziv-Welch coding. Experimental results show that the proposed probabilitybased source coding has better values of the compression ratio to the time required for all the messages considered.

Plagiarism occurs when the content is copied without permission or citation. One of the contributing factors is that many text documents on the internet are easily copied and accessed. This paper introduces a plagiarism detection technique based on the Semantic Role Labeling (SRL). The technique analyses and compares text based on the semantic allocation for each term inside the sentence. SRL is superior in generating arguments for each sentence semantically. Weighting for each argument generated by SRL to study its behaviour is also introduced in this paper. It was found that not all arguments affect the plagiarism detection process. In addition, experimental results on PAN-PC-09 data sets showed that our method significantly outperforms the modern methods for plagiarism detection in terms of Recall, Precision and F-measure.

The Flight ticket prices increase or decrease every now and then depending on various factors like timing of the flights, destination, duration of flights. In the proposed system a predictive model will be created by applying machine learning algorithms to the collected historical data of flights. Optimal timing for airline ticket purchasing from the consumer's perspective is challenging principally because buyers have insufficient information for reasoning about future price movements. In this project we majorly targeted to uncover underlying trends of flight prices in India using historical data and also to suggest the best time to buy a flight ticket. The project implements the validations or contradictions towards myths regarding the airline industry, a comparison study among various models in predicting the optimal time to buy the flight ticket and the amount that can be saved if done so. Remarkably, the trends of the prices are highly sensitive to the route, month of departure, day of departure, time of departure, whether the day of departure is a holiday and airline carrier. Highly competitive routes like most business routes (tier 1 to tier 1 cities like Mumbai-Delhi) had a non-decreasing trend where prices increased as days to departure decreased, however other routes (tier 1 to tier 2 cities like Delhi-Guwahati) had a specific time frame where the prices are minimum. Moreover, the data also uncovered two basic categories of airline carriers operating in India-the economical group and the luxurious group, and in most cases, the minimum priced flight was a member of the economical group. The data also validated the fact that, there are certain time-periods of the day where the prices are expected to be maximum. The scope of the project can be extensively extended across the various routes to make significant savings on the purchase of flight prices across the Indian Domestic Airline market.

In this paper, a energy-efficient data collection method is proposed in which an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be transformed in DCT domain, we propose that each cluster from the networks only sends a small number of large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the large coefficients received at the BS. We further analyze and formulate the communication cost as the power consumption for transmitting data in such networks based on stochastic problems. Some common clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise and noiseless environments for the proposed method are considered.

This work proposes a preprocessing method for image compression based on attribute filtering. This method is completely shape preserving and computationally cheap. Three filters were investigated, including one derived from the power filter of Evans and Young that removes even more perceptually unimportant information. The results from 22 images that were processed in various ways and compressed using the popular compression algorithms of Jpeg, Jpeg2000 and LZW are presented. Our experiments have shown that all the filters cause an improvement of as much as 11, 10 and 20% for jpeg, jpeg2000 and LZW algorithms respectively.

The optimal configuration for a Large Scale Wireless Sensor Networks (LS-WSN) is the one that minimizes the sampling rate, the CPU time and the channel accesses (thus maximizing the network lifetime), with a controlled distortion in the recovered data. Initial deployments of LS-WSN are usually not able to adapt to changing environments and rarely take into account either the spatial or temporal nature of the sensed variables, both techniques that optimize the network operation. In this work we propose the use of Self-Organized Distributed Compressive Projection (SODCP) in order to let the nodes to form clusters in a distributed and data-driven way, exploiting the spatial correlation of the sensed data. We compare the performance of this innovative technique, using actual data from the LUCE LS-WSN, with two different baselines: Centralized Compressive Projection (CCP) and Distributed Compressive Projection (DCP). The former uses no clustering, whereas the latter makes use of an a priori clustering that favors proximity and balances the number of nodes in each cluster. We show that SODCP outperforms DCP (in terms of Signal-to-Noise vs. Compression Rate). We also show that the performance of SODCP converges to that of CCP for relatively high compression rates of 55%.

In this paper, a energy-efficient data collection method is proposed in which an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be transformed in DCT domain, we propose that each cluster from the networks only sends a small number of large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the large coefficients received at the BS. We further analyze and formulate the communication cost as the power consumption for transmitting data in such networks based on stochastic problems. Some common clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise and noiseless environments for the proposed method are considered.

Insurance is a policy that helps to cover up all loss or decrease loss in terms of expenses incurred by various risks. A number of variables affect how much insurance costs. These considerations of different factors contribute to the insurance policy cost expression. Machine Learning(ML) in the insurance sector can make insurance more effective. In the domains of computational and applied mathematics the machine learning (ML) is a well-known research area. ML is one of the computational intelligence aspects when it comes to exploitation of historical data that may be addressed in a wide range of applications and systems. There are some limitations in ML so; Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry and thus it requires few more investigation and improvement. Using the machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The proposed research approach uses Linear Regression, Decision Tree Regression and Gradient Boosting Regression and also streamlit as a framework. We had used a medical insurance cost dataset that was acquired from the KAGGLE repository for the cost prediction purpose, and machine learning methods are used to show the forecasting of insurance costs by regression model comparing their accuracies.

Wireless sensor network consists of large number of wireless node that are responsible for sensing processing and monitoring environmental .These sensor nodes are battery operated. Clustering is a standard approach for achieving efficient and scalable performance in WSN. Compressive sensing (CS) reduces the amount of data transmission and balances the load of traffic throughout the network. In WSN the total number of transmission for collection of data using pure CS is large. Therefore we are using the hybrid CS method to reduce the number of transmission in sensor network. Though the previous work uses the CS on routing trees method. In this paper, we propose a clustering method that uses hybrid CS for WSN. Clusters are formed by group of sensor nodes where each sensor node in cluster sends their data to cluster head(CH) without using CS, further using CS each CH transmits their data to sink. An analytical model shows the relationship between the number of transmission and cluster ...