Dr. Shahid NaseemAssistant Professor (CS Phone: 92-300-5305627 Address: Department of Civil Engineering, University College of Engineering, Sciences & Technology, Lahore Leads University, Westwood Colony, Main Raiwind Road, Thokar Niaz Baig, Lahore, Pakistan.
Background: The new improvements in hardware and machines showing shrewd qualities includes vario... more Background: The new improvements in hardware and machines showing shrewd qualities includes various procedures comprising software and hardware architectural improvements. A wide range of wearable-sensors, hardware equipment, machine and deep-learning models are being applied in Human Activity Recognition (HAR) oriented systems and applications lately. Whereas, to foster best models for accurate classification of human actions is of critical significance. Results: For the accomplishment of this objective this study utilizes sensor’s data from two less-expensive sensors, accelerometer, and gyroscope alongside the execution of reconstruction based feature encoding approach i.e. Locality-constrained Linear Coding (LLC) for human activity recognition. This research is intended to perform human action classification where LLC is used in this research for encoding the discriminative data of human body movements (acquired through sensors) while performing a specific action. For encoding th...
Purpose---The purpose of this paper is to develop an “Intrusion Detection Architecture for Distri... more Purpose---The purpose of this paper is to develop an “Intrusion Detection Architecture for Distributed Systems using Game Theory Approach” for interaction between nodes (normal or malicious) and IDS as a repeated game. Methodology--We inject a small sample of simulated attacks into the network traffic and use the system response to these attacks to define the game structure and utility functions. N players play initially cooperative and then non-cooperative game at each stage of the game, where players of the game are an IDS and nodes. Findings-It is found that Intrusion Detection Architecture for distributed systems by using the game theory approach provides the results more than direct optimization methods. It verifies real-world attacks performed on the monitored network by using both Max-Min and Nash equilibrium. The advantage of this approach is not only in its security, but also in better model characteristics in terms of strategy space coverage (unfrequented, but critical att...
Cloud computing is a technology that provides secure storage space for the customer's massive dat... more Cloud computing is a technology that provides secure storage space for the customer's massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client's data. They have to be dependent on cloud service providers for assurance of the platform's security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most ... more Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud service...
Lahore Garrison University Research Journal of Computer Science and Information Technology, 2017
Since last some decades, the autonomous technology in the vehicles was used to help the drivers t... more Since last some decades, the autonomous technology in the vehicles was used to help the drivers to voyage effortlessly along the highways and to avoid road accidents. In this duration, a number of high-end vehicles was built-in electronic secureness mechanism, adaptive voyage mechanism, lane departure warnings and city safety system. Approximately, 95% of the road accidents were caused by the wrong behavior, careless, focus less and tiredness of the drivers. Even with the attractiveness of recent traffic control applications, a lack of dynamic information about roads and weather conditions was more than an infuriation. For this purpose, we developed a self-enabling vehicular agent in which cloud and Massive data is used. The cloud and massive data enabled the agent to see around corners or even miles down the road and to drive itself more carefully. The parameters enabled the agent to keep the driver informed on the road conditions ahead. The vehicular agent would be enable to proce...
Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generati... more Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the pri...
Background: The new improvements in hardware and machines showing shrewd qualities includes vario... more Background: The new improvements in hardware and machines showing shrewd qualities includes various procedures comprising software and hardware architectural improvements. A wide range of wearable-sensors, hardware equipment, machine and deep-learning models are being applied in Human Activity Recognition (HAR) oriented systems and applications lately. Whereas, to foster best models for accurate classification of human actions is of critical significance. Results: For the accomplishment of this objective this study utilizes sensor’s data from two less-expensive sensors, accelerometer, and gyroscope alongside the execution of reconstruction based feature encoding approach i.e. Locality-constrained Linear Coding (LLC) for human activity recognition. This research is intended to perform human action classification where LLC is used in this research for encoding the discriminative data of human body movements (acquired through sensors) while performing a specific action. For encoding th...
Purpose---The purpose of this paper is to develop an “Intrusion Detection Architecture for Distri... more Purpose---The purpose of this paper is to develop an “Intrusion Detection Architecture for Distributed Systems using Game Theory Approach” for interaction between nodes (normal or malicious) and IDS as a repeated game. Methodology--We inject a small sample of simulated attacks into the network traffic and use the system response to these attacks to define the game structure and utility functions. N players play initially cooperative and then non-cooperative game at each stage of the game, where players of the game are an IDS and nodes. Findings-It is found that Intrusion Detection Architecture for distributed systems by using the game theory approach provides the results more than direct optimization methods. It verifies real-world attacks performed on the monitored network by using both Max-Min and Nash equilibrium. The advantage of this approach is not only in its security, but also in better model characteristics in terms of strategy space coverage (unfrequented, but critical att...
Cloud computing is a technology that provides secure storage space for the customer's massive dat... more Cloud computing is a technology that provides secure storage space for the customer's massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client's data. They have to be dependent on cloud service providers for assurance of the platform's security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most ... more Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud service...
Lahore Garrison University Research Journal of Computer Science and Information Technology, 2017
Since last some decades, the autonomous technology in the vehicles was used to help the drivers t... more Since last some decades, the autonomous technology in the vehicles was used to help the drivers to voyage effortlessly along the highways and to avoid road accidents. In this duration, a number of high-end vehicles was built-in electronic secureness mechanism, adaptive voyage mechanism, lane departure warnings and city safety system. Approximately, 95% of the road accidents were caused by the wrong behavior, careless, focus less and tiredness of the drivers. Even with the attractiveness of recent traffic control applications, a lack of dynamic information about roads and weather conditions was more than an infuriation. For this purpose, we developed a self-enabling vehicular agent in which cloud and Massive data is used. The cloud and massive data enabled the agent to see around corners or even miles down the road and to drive itself more carefully. The parameters enabled the agent to keep the driver informed on the road conditions ahead. The vehicular agent would be enable to proce...
Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generati... more Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. It faces performance deterioration and quality of service (QoS) degradation problems, especially in the Internet of Things (IoT) based scenarios. Therefore, existing caching strategies need to be enhanced to augment the cache hit ratio and manage the limited storage to accelerate content deliveries. Alternatively, quantum computing (QC) appears to be a prospect of more or less every typical computing problem. The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). Firstly, the DL agent prioritizes caching contents via self organizing maps (SOMs) algorithm, and secondly, the pri...
Abstract--- Ontologies can be built on systems that can evolve continuously. Ontologies analysi... more Abstract--- Ontologies can be built on systems that can evolve continuously. Ontologies analysis and appraisal are performed for measuring the eminence of an ontology. Ontologies evaluation is used to automatically reveal conceivability problems. For evaluating dynamic ontologies in semantic web and to set the weights to the neurons, Classical Neural Network (CNN) has to face a number of challenges such as the absence of discrete algorithm, limited memory capacity, time-consuming training due to the declarative nature of ontologies. CNN cannot provide low-cost learning. In CNN, data is non-linear and hard to analyze. Recently, with the rapid development of technology, there are a lot of applications such as dynamic ontologies creation and evaluation require to low-slung learning cost. However, the computational power of CNN, is that it cannot provide low-slung learning cost. On the other hand, Quantum Neural Network (QNN) can be a good computational network instead of CNN approaches. In this paper, we present a new computational approach for dynamic ontologies evaluation to the Quantum Perceptron Neural Network (QPNN) can achieve low-slung learning cost. The proposed Quantum Neural Network can construct self-adaptive activation operators that have the capability to accomplish the learning process in a limited number of iterations and thereby, reduce the overall computational cost. The proposed approach is capable to construct its own set of activation operators to be applied widely in both quantum and classical applications to overcome the linearity limitation of classical perceptron.
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