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Companies outsource a lot of their development tasks. The use of external development teams introduces security problems which may lead to data breaches and even corporate espionage where business ideas are used in other companies, leading to leaking of trade secrets. A detailed explanation of the security implications of outsourcing is given, with ways to mitigate such risks in the first section of the report. The report also explains some basics theory in cyber security such as information gathering, vulnerability scanning, exploitation and post exploitation. We also look at some software tools used in the field. Due to the lack of knowledge and awareness about cyber security, most small companies do not have enough protection against these malicious attacks. The proposed intrusion detection system is capable of recognizing various kinds of cyber attacks including denial of serviceattack, spoofing attack, sniffing attack and so on. The proposed system employs ensemble learning and feature selection techniques to reduce the computational cost and improve the detection rate simultaneously. This paper presents an intelligent intrusion detection system based on tree-structure machine learning models. After the implementation of the proposed intrusion detection system on standard data sets, the system has achieved high detection rate and low computational cost simultaneously. The method used to bring results is python with scikit library that can help with machine learning. The results will show figures of heatmap and scores of models that will explain how likely it will identify a cyber attack.

This study is carried out to investigate the impact of taxes on the economic growth of Pakistan. Tax is a compulsory payment to the government by the residents of the nation to cover the cost of services rendered by the government for the general welfare of its citizens. The debate and empirical results regarding the impact of taxes and specifically tax structure is highly controversial for researchers and policymakers. Economic growth is adversely affected by taxes, while some researchers claim a positive effect. Therefore it was felt to investigate the impact of tax structure on the economic growth of Pakistan. To test it empirically, the researcher took the time series data ranges from 1985 to 2021 on tax structure and economic growth. GDP growth is dependent, while tax structure, i.e., direct and indirect taxes, human development index, and income distribution, were selected as independent variables. After ensuring that the variables are stationary, the Autoregressive Distributive Lag (ARDL) approach to co-integration was applied to detect connections between variables. Results found that direct taxes have a positive and significant effect on GDP growth, while indirect taxes have a significant and negative impact on the GDP growth of Pakistan. Also, the impact of HDI on GDP growth is positive and significant, while the Gini coefficient has a negative and significant impact on the GDP growth of Pakistan. It is suggested that to increase the economic growth of Pakistan, direct taxes would be increased, as it will also reduce the Gini coefficient and unequal income distribution, While indirect taxes would be decreased to enhance economic growth. Also, the government should adopt such policies which could encourage human development, as it is crucial for the economic development of Pakistan.

This study is carried out to investigate the impact of taxes on the economic growth of Pakistan. Tax is a compulsory payment to the government by the residents of the nation to cover the cost of services rendered by the government for the general welfare of its citizens. The debate and empirical results regarding the impact of taxes and specifically tax structure is highly controversial for researchers and policymakers. Economic growth is adversely affected by taxes, while some researchers claim a positive effect. Therefore it was felt to investigate the impact of tax structure on the economic growth of Pakistan. To test it empirically, the researcher took the time series data ranges from 1985 to 2021 on tax structure and economic growth. GDP growth is dependent, while tax structure, i.e., direct and indirect taxes, human development index, and income distribution, were selected as independent variables. After ensuring that the variables are stationary, the Autoregressive Distributive Lag (ARDL) approach to co-integration was applied to detect connections between variables. Results found that direct taxes have a positive and significant effect on GDP growth, while indirect taxes have a significant and negative impact on the GDP growth of Pakistan. Also, the impact of HDI on GDP growth is positive and significant, while the Gini coefficient has a negative and significant impact on the GDP growth of Pakistan. It is suggested that to increase the economic growth of Pakistan, direct taxes would be increased, as it will also reduce the Gini coefficient and unequal income distribution, While indirect taxes would be decreased to enhance economic growth. Also, the government should adopt such policies which could encourage human development, as it is crucial for the economic development of Pakistan.

Companies outsource a lot of their development tasks. The use of external development teams introduces security problems which may lead to data breaches and even corporate espionage where business ideas are used in other companies, leading to leaking of trade secrets. A detailed explanation of the security implications of outsourcing is given, with ways to mitigate such risks in the first section of the report. The report also explains some basics theory in cyber security such as information gathering, vulnerability scanning, exploitation and post exploitation. We also look at some software tools used in the field. Due to the lack of knowledge and awareness about cyber security, most small companies do not have enough protection against these malicious attacks. The proposed intrusion detection system is capable of recognizing various kinds of cyber attacks including denial of serviceattack, spoofing attack, sniffing attack and so on. The proposed system employs ensemble learning and feature selection techniques to reduce the computational cost and improve the detection rate simultaneously. This paper presents an intelligent intrusion detection system based on tree-structure machine learning models. After the implementation of the proposed intrusion detection system on standard data sets, the system has achieved high detection rate and low computational cost simultaneously. The method used to bring results is python with scikit library that can help with machine learning. The results will show figures of heatmap and scores of models that will explain how likely it will identify a cyber attack.

Bridges are a critical component of transportation network. The long-term maintenance of bridges represent around 30% of the financial value of transport infrastructure. As the bridge ages, it requires adequate maintenance against deterioration to ensure safety and serviceability. Therefore, periodic inspections for regular condition monitoring are vital for timely implementation of the maintenance strategies. The purpose of this doctoral research is to reduce the burden of data collection for bridge management systems (BMS) by developing innovative non-destructive testing (NDT) techniques, which could quickly check the bridge elements for damage identification. This study mainly focused on the bridge concrete deck and bearings. Current bridge deterioration models are based on the condition state of the individual elements of the bridge. The bridge condition data is conventionally collected through visual and physical inspection of the respective elements, which are labour intensive and expensive in term of time and money. Furthermore, these techniques are subjective and unable to detect the concealed subsurface defects, which could be more expensive to repair later on if they are unable to be detected in time. For the effective bridge condition rating, the integration of NDT techniques and the conventional visual inspections could potentially overcome the deficiencies associated with the conventional inspection methods. The largest portion of expenditures on bridge maintenance goes to the deck only. Subsurface delamination in concrete bridge decks is a widespread problem due to the corrosion of reinforcement in concrete deck. Infrared thermography (IRT) has been identified as an effective NDT technique that can remotely scan the concrete bridge members for subsurface delamination detection. However, there were still several uncertainties and deficiencies in using IRT for efficient bridge inspections. These uncertainties are highlighted and the potential advancement

Our thesis work is based on revealing minor informative variations in a video which are hard to perceive, that can be further exaggerated to extract hidden variations of color and motions in a video. In our thesis we apply di erent techniques of a video decomposition like Laplacian, Steerable and Gaussian pyramids to observe the improvement in performance of the videos. We start with a standard input video to decompose it in di erent spatial pool of frequencies, the temporal ltering process is applied to the frames to extract hidden signals. The resultant signals from the temporal processing are then ampli ed by a given factor to reveal hidden information in the videos. These ampli ed signals are added back to the original signals and then a pyramid is collapsed to generate an output video. Performance of Gaussian and Steerable pyramids for video decomposition is evaluated over Eulerian motion magni cation. The output videos from all pyramids decomposition is computationally analyzed and compared with each other through SSIM and PSNR graphs. The video processing time is used to compare decomposition methods. It is observed that Eulerian motion magni cation with Steerable pyramid decomposition has potential of revealing hidden motions more than Laplacian and Gaussian pyramids, precisely in monitoring and diagnostic applications. Steerable pyramid decomposition method performs better than the other methods when input video is noisy., A standard video has been taken as input and magni ed to amplify the small motion in it which was invisible to human eye. This method processes pixels at speci c positions in a video where it has got low frequencies and ampli es them to see small changes in video work improvements in Eulerian motion magni cation and color ampli cation by using Gaussian and Steerable pyramids for video decomposition were investigated. To observe small changes in a video and to extract subtle changes Steerable and Gaussian pyramids were used. The effects on di erent videos with di erent formats and noise environments when subjected to this system were observed. Proposed model was found to be better in performance then contemporary Eulerian magni cation using Laplacian pyramid in some cases. Magni cation using Steerable pyramid showed better results than Laplacian, as seen from PSNR, SSIM graphs and comparison., 926/B Peoples colony # 1 Faisalabad,Pakistan Phone Number # +92-300-6601636

Our thesis work is based on revealing minor informative variations in a video which are hard to perceive, that can be further exaggerated to extract hidden variations of color and motions in a video. In our thesis we apply di erent techniques of a video decomposition like Laplacian, Steerable and Gaussian pyramids to observe the improvement in performance of the videos. We start with a standard input video to decompose it in di erent spatial pool of frequencies, the temporal ltering process is applied to the frames to extract hidden signals. The resultant signals from the temporal processing are then ampli ed by a given factor to reveal hidden information in the videos. These ampli ed signals are added back to the original signals and then a pyramid is collapsed to generate an output video. Performance of Gaussian and Steerable pyramids for video decomposition is evaluated over Eulerian motion magni cation. The output videos from all pyramids decomposition is computationally analyzed and compared with each other through SSIM and PSNR graphs. The video processing time is used to compare decomposition methods. It is observed that Eulerian motion magni cation with Steerable pyramid decomposition has potential of revealing hidden motions more than Laplacian and Gaussian pyramids, precisely in monitoring and diagnostic applications. Steerable pyramid decomposition method performs better than the other methods when input video is noisy., A standard video has been taken as input and magni ed to amplify the small motion in it which was invisible to human eye. This method processes pixels at speci c positions in a video where it has got low frequencies and ampli es them to see small changes in video work improvements in Eulerian motion magni cation and color ampli cation by using Gaussian and Steerable pyramids for video decomposition were investigated. To observe small changes in a video and to extract subtle changes Steerable and Gaussian pyramids were used. The effects on di erent videos with di erent formats and noise environments when subjected to this system were observed. Proposed model was found to be better in performance then contemporary Eulerian magni cation using Laplacian pyramid in some cases. Magni cation using Steerable pyramid showed better results than Laplacian, as seen from PSNR, SSIM graphs and comparison., 926/B Peoples colony # 1 Faisalabad,Pakistan Phone Number # +92-300-6601636

Our thesis work is based on revealing minor informative variations in a video which are hard to perceive, that can be further exaggerated to extract hidden variations of color and motions in a video. In our thesis we apply di erent techniques of a video decomposition like Laplacian, Steerable and Gaussian pyramids to observe the improvement in performance of the videos. We start with a standard input video to decompose it in di erent spatial pool of frequencies, the temporal ltering process is applied to the frames to extract hidden signals. The resultant signals from the temporal processing are then ampli ed by a given factor to reveal hidden information in the videos. These ampli ed signals are added back to the original signals and then a pyramid is collapsed to generate an output video. Performance of Gaussian and Steerable pyramids for video decomposition is evaluated over Eulerian motion magni cation. The output videos from all pyramids decomposition is computationally analyzed and compared with each other through SSIM and PSNR graphs. The video processing time is used to compare decomposition methods. It is observed that Eulerian motion magni cation with Steerable pyramid decomposition has potential of revealing hidden motions more than Laplacian and Gaussian pyramids, precisely in monitoring and diagnostic applications. Steerable pyramid decomposition method performs better than the other methods when input video is noisy., A standard video has been taken as input and magni ed to amplify the small motion in it which was invisible to human eye. This method processes pixels at speci c positions in a video where it has got low frequencies and ampli es them to see small changes in video work improvements in Eulerian motion magni cation and color ampli cation by using Gaussian and Steerable pyramids for video decomposition were investigated. To observe small changes in a video and to extract subtle changes Steerable and Gaussian pyramids were used. The effects on di erent videos with di erent formats and noise environments when subjected to this system were observed. Proposed model was found to be better in performance then contemporary Eulerian magni cation using Laplacian pyramid in some cases. Magni cation using Steerable pyramid showed better results than Laplacian, as seen from PSNR, SSIM graphs and comparison., 926/B Peoples colony # 1 Faisalabad,Pakistan Phone Number # +92-300-6601636

Our thesis work is based on revealing minor informative variations in a video which are hard to perceive, that can be further exaggerated to extract hidden variations of color and motions in a video. In our thesis we apply di erent techniques of a video decomposition like Laplacian, Steerable and Gaussian pyramids to observe the improvement in performance of the videos. We start with a standard input video to decompose it in di erent spatial pool of frequencies, the temporal ltering process is applied to the frames to extract hidden signals. The resultant signals from the temporal processing are then ampli ed by a given factor to reveal hidden information in the videos. These ampli ed signals are added back to the original signals and then a pyramid is collapsed to generate an output video. Performance of Gaussian and Steerable pyramids for video decomposition is evaluated over Eulerian motion magni cation. The output videos from all pyramids decomposition is computationally analyzed and compared with each other through SSIM and PSNR graphs. The video processing time is used to compare decomposition methods. It is observed that Eulerian motion magni cation with Steerable pyramid decomposition has potential of revealing hidden motions more than Laplacian and Gaussian pyramids, precisely in monitoring and diagnostic applications. Steerable pyramid decomposition method performs better than the other methods when input video is noisy., A standard video has been taken as input and magni ed to amplify the small motion in it which was invisible to human eye. This method processes pixels at speci c positions in a video where it has got low frequencies and ampli es them to see small changes in video work improvements in Eulerian motion magni cation and color ampli cation by using Gaussian and Steerable pyramids for video decomposition were investigated. To observe small changes in a video and to extract subtle changes Steerable and Gaussian pyramids were used. The effects on di erent videos with di erent formats and noise environments when subjected to this system were observed. Proposed model was found to be better in performance then contemporary Eulerian magni cation using Laplacian pyramid in some cases. Magni cation using Steerable pyramid showed better results than Laplacian, as seen from PSNR, SSIM graphs and comparison., 926/B Peoples colony # 1 Faisalabad,Pakistan Phone Number # +92-300-6601636

Our thesis work is based on revealing minor informative variations in a video which are hard to perceive, that can be further exaggerated to extract hidden variations of color and motions in a video. In our thesis we apply di erent techniques of a video decomposition like Laplacian, Steerable and Gaussian pyramids to observe the improvement in performance of the videos. We start with a standard input video to decompose it in di erent spatial pool of frequencies, the temporal ltering process is applied to the frames to extract hidden signals. The resultant signals from the temporal processing are then ampli ed by a given factor to reveal hidden information in the videos. These ampli ed signals are added back to the original signals and then a pyramid is collapsed to generate an output video. Performance of Gaussian and Steerable pyramids for video decomposition is evaluated over Eulerian motion magni cation. The output videos from all pyramids decomposition is computationally analyzed and compared with each other through SSIM and PSNR graphs. The video processing time is used to compare decomposition methods. It is observed that Eulerian motion magni cation with Steerable pyramid decomposition has potential of revealing hidden motions more than Laplacian and Gaussian pyramids, precisely in monitoring and diagnostic applications. Steerable pyramid decomposition method performs better than the other methods when input video is noisy., A standard video has been taken as input and magni ed to amplify the small motion in it which was invisible to human eye. This method processes pixels at speci c positions in a video where it has got low frequencies and ampli es them to see small changes in video work improvements in Eulerian motion magni cation and color ampli cation by using Gaussian and Steerable pyramids for video decomposition were investigated. To observe small changes in a video and to extract subtle changes Steerable and Gaussian pyramids were used. The effects on di erent videos with di erent formats and noise environments when subjected to this system were observed. Proposed model was found to be better in performance then contemporary Eulerian magni cation using Laplacian pyramid in some cases. Magni cation using Steerable pyramid showed better results than Laplacian, as seen from PSNR, SSIM graphs and comparison., 926/B Peoples colony # 1 Faisalabad,Pakistan Phone Number # +92-300-6601636

Our thesis work is based on revealing minor informative variations in a video which are hard to perceive, that can be further exaggerated to extract hidden variations of color and motions in a video. In our thesis we apply di erent techniques of a video decomposition like Laplacian, Steerable and Gaussian pyramids to observe the improvement in performance of the videos. We start with a standard input video to decompose it in di erent spatial pool of frequencies, the temporal ltering process is applied to the frames to extract hidden signals. The resultant signals from the temporal processing are then ampli ed by a given factor to reveal hidden information in the videos. These ampli ed signals are added back to the original signals and then a pyramid is collapsed to generate an output video. Performance of Gaussian and Steerable pyramids for video decomposition is evaluated over Eulerian motion magni cation. The output videos from all pyramids decomposition is computationally analyzed and compared with each other through SSIM and PSNR graphs. The video processing time is used to compare decomposition methods. It is observed that Eulerian motion magni cation with Steerable pyramid decomposition has potential of revealing hidden motions more than Laplacian and Gaussian pyramids, precisely in monitoring and diagnostic applications. Steerable pyramid decomposition method performs better than the other methods when input video is noisy., A standard video has been taken as input and magni ed to amplify the small motion in it which was invisible to human eye. This method processes pixels at speci c positions in a video where it has got low frequencies and ampli es them to see small changes in video work improvements in Eulerian motion magni cation and color ampli cation by using Gaussian and Steerable pyramids for video decomposition were investigated. To observe small changes in a video and to extract subtle changes Steerable and Gaussian pyramids were used. The effects on di erent videos with di erent formats and noise environments when subjected to this system were observed. Proposed model was found to be better in performance then contemporary Eulerian magni cation using Laplacian pyramid in some cases. Magni cation using Steerable pyramid showed better results than Laplacian, as seen from PSNR, SSIM graphs and comparison., 926/B Peoples colony # 1 Faisalabad,Pakistan Phone Number # +92-300-6601636