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Mohsen Guizani

Bio: Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.


Papers
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TL;DR: A new linear dynamic programming algorithm using backward induction and interchange arguments to minimize the dissemination latency of the vehicles is proposed and a closed form of dissemination latency in vehicular platoon is obtained by utilizing Markov chain with M/M/1 queuing model.
Abstract: In a vehicular platoon, a lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates control commands to following vehicles based on vehicle-to-vehicle communications. However, reducing command dissemination latency with multiple vehicles while ensuring successful message delivery to the tail vehicle is challenging. We propose a new linear dynamic programming algorithm using backward induction and interchange arguments to minimize the dissemination latency of the vehicles. Furthermore, a closed form of dissemination latency in vehicular platoon is obtained by utilizing Markov chain with M/M/1 queuing model. Simulation results confirm that the proposed dynamic programming algorithm improves the dissemination rate by at least 50.9%, compared to similar algorithms in the literature. Moreover, it also approximates the best performance with the maximum gap of up to 0.2 second in terms of latency.

4 citations

Proceedings ArticleDOI
20 May 2019
TL;DR: A cross-lingual multi-keyword rank search scheme which eliminates the barrier of languages and achieves semantic extension with using the Open Multilingual Wordnet and also realizes intelligent and personalized search through flexible keyword and language preference settings.
Abstract: Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption (MRSE) framework based on Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to previous MRSE schemes constructed based on the k-nearest neighbor searchable encryption (KNN-SE) algorithm, it can mitigate some drawbacks and achieve better performance in terms of functionality and efficiency. Additionally, it does not require a predefined keyword set and support keywords in arbitrary languages. However, due to the pattern of exact matching of keywords in the new MRSE scheme, multilingual search is limited to each language and cannot be searched across languages. In this paper, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme which eliminates the barrier of languages and achieves semantic extension with using the Open Multilingual Wordnet. Our CLRSE scheme also realizes intelligent and personalized search through flexible keyword and language preference settings. We evaluate the performance of our scheme in terms of security, functionality, precision and efficiency, via extensive experiments.

4 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: A QoSuS model for Mobile Ad Hoc Networks (MANET) is designed to reduce as much harm as possible when a disaster strikes and optimization problems that are proposed for high QoSus of MANET in three-tier cellular networks are proposed.
Abstract: During a disaster, communication systems are partially (or completely) interrupted with very limited resources due to infrastructures destruction and hence the lack of essential services. Meanwhile, the demand for communication reaches its highest peak ever since users need to contact loved ones and make sure they are safe, inform first responders and local governments about surrounding conditions, and receive urgent instructions for safety and resilience. Rather than only high quality of service (QoS), the affected users now require high quality of sustainability (QoSus) of communications that are characterized by high response, interoperable and robust connections, high hit rate and delivery capacity of contents, and resource savings. In this paper, we therefore design a QoSuS model for Mobile Ad Hoc Networks (MANET) to reduce as much harm as possible when a disaster strikes. To this end, we propose optimization problems that we solve for high QoSus of MANET in three-tier cellular networks (i.e., macrocells, femtocells, and mobile devices). Our optimal results include a number of replicas of each cached content, a set of femtocells to cache the replicas, a set of cellular users to share their sub-channels with other mobile devices, and a set of relay nodes for mobile device-to-device communications. We further take into account the constraints of co-channel interference due to many simultaneous channel accesses, storage resource of femtocells, and energy resource of mobile devices, to efficiently gain high performance of solution. Simulation results demonstrate that our proposed solutions provide the best possible QoSus under such severe conditions.

3 citations

Proceedings ArticleDOI
22 May 1991
TL;DR: An outline is presented of the development process of the intelligent load flow engine, which proceeds from simple to hard tasks by incrementally improving the organization and the representation of the knowledge base.
Abstract: An outline is presented of the development process of the intelligent load flow engine. Building the engine proceeds from simple to hard tasks by incrementally improving the organization and the representation of the knowledge base. Interface programs are designed to provide the communication from the engine to a load flow and contingency analysis program. >

3 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered an air-ground network in which the UAV flies straightly to collect information from the IoT devices in a 2D plane based on the CSMA/CA protocol.
Abstract: Unmanned aerial vehicles (UAVs) have played an important role in air-ground integration network. Especially in Internet of Things (IoT) services, UAV equipped with communication equipments is widely adopted as a mobile base station (BS) for data collection from IoT devices on the ground. In this paper, we consider an air-ground network in which the UAV flies straightly to collect information from the IoT devices in a 2-D plane based on the CSMA/CA protocol. Due to UAV's continuous mobility, the communication durations of devices in different locations with UAV are not only time-limited, but also vary from each other. To analyze the throughput performance of uplink multiple access control (MAC) protocol, we propose a new analysis model to deal with the communications heterogeneity in the network. Firstly, we divide the devices in the coverage into different clusters according to their communication durations. Then, a quitting probability indicating the probability that a device quits the UAV's coverage at each time slot is clarified. A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis. Besides, we also propose a modified CSMA/CA protocol which fully considers the heterogeneity of the access time and adaptively allocates the time resource among the devices in different clusters. Finally, the effects of retry limit, initial contention window size, the density of the devices, UAVs speed and coverage area are discussed in the simulation section.

3 citations


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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2002

9,314 citations