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M. Z. Rashad

Publications and source records attributed to M. Z. Rashad.

12 recordsLinked to original sources
arXiv2013

Food Recommendation using Ontology and Heuristics

Recommender systems are needed to find food items of ones interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.

cs.IR↗
arXiv2013

Spike and Tyke, the Quantized Neuron Model

Modeling spike firing assumes that spiking statistics are Poisson, but real data violates this assumption. To capture non-Poissonian features, in order to fix the inevitable inherent irregularity, researchers rescale the time axis with tedious computational overhead instead of searching for another distribution. Spikes or action potentials are precisely-timed changes in the ionic transport through synapses adjusting the synaptic weight, successfully modeled and developed as a memristor. Memristance value is multiples of initial resistance. This reminds us with the foundations of quantum mechanics. We try to quantize potential and resistance, as done with energy. After reviewing Planck curve for blackbody radiation, we propose the quantization equations. We introduce and prove a theorem that quantizes the resistance. Then we define the tyke showing its basic characteristics. Finally we give the basic transformations to model spiking and link an energy quantum to a tyke. Investigation shows how this perfectly models the neuron spiking, with over 97% match.

cs.NE↗
arXiv2013

Overcoming Misleads In Logic Programs by Redefining Negation

Negation as failure and incomplete information in logic programs have been studied by many researchers In order to explains HOW a negated conclusion was reached, we introduce and proof a different way for negating facts to overcoming misleads in logic programs. Negating facts can be achieved by asking the user for constants that do not appear elsewhere in the knowledge base.

cs.AI↗
arXiv2013

Improving problem solving by exploiting the concept of symmetry

We investigate the concept of symmetry and its role in problem solving. This paper first defines precisely the elements that constitute a "problem" and its "solution," and gives several examples to illustrate these definitions. Given precise definitions of problems, it is relatively straightforward to construct a search process for finding solutions. Finally this paper attempts to exploit the concept of symmetry in improving problem solving.

cs.AI↗
arXiv2013

Quantum-inspired Huffman Coding

Huffman Compression, also known as Huffman Coding, is one of many compression techniques in use today. The two important features of Huffman coding are instantaneousness that is the codes can be interpreted as soon as they are received and variable length that is a most frequent symbol has length smaller than a less frequent symbol. The traditional Huffman coding has two procedures: constructing a tree in O(n^2) and then traversing it in O(n). Quantum computing is a promising approach of computation that is based on equations from Quantum Mechanics. Instantaneousness and variable length features are difficult to generalize to the quantum case. The quantum coding field is pioneered by Schumacher works on block coding scheme. To encode N signals sequentially, it requires O(N3) computational steps. The encoding and decoding processes are far from instantaneous. Moreover, the lengths of all the codewords are the same. A Huffman-coding-inspired scheme for the storage of quantum information takes O(N(log N)a) computational steps for a sequential implementation on non-parallel machines.

cs.IT↗
arXiv2013

Q#, a quantum computation package for the .NET platform

Quantum computing is a promising approach of computation that is based on equations from Quantum Mechanics. A simulator for quantum algorithms must be capable of performing heavy mathematical matrix transforms. The design of the simulator itself takes one of three forms: Quantum Turing Machine, Network Model or circuit model of connected gates or, Quantum Programming Language, yet, some simulators are hybrid. We studied previous simulators and then we adopt features from three simulators of different implementation languages, different paradigms, and for different platforms. They are Quantum Computing Language (QCL), QUASI, and Quantum Optics Toolbox for Matlab 5. Our simulator for quantum algorithms takes the form of a package or a programming library for Quantum computing, with a case study showing the ability of using it in the circuit model. The .NET is a promising platform for computing. VB.NET is an easy, high productive programming language with the full power and functionality provided by the .NET framework. It is highly readable, writeable, and flexible language, compared to another language such as C#.NET in many aspects. We adopted VB.NET although its shortage in built-in mathematical complex and matrix operations, compared to Matlab. For implementation, we first built a mathematical core of matrix operations. Then, we built a quantum core which contains: basic qubits and register operations, basic 1D, 2D, and 3D quantum gates, and multi-view visualization of the quantum state, then a window for demos to show you how to use and get the most of the package.

cs.ET↗
arXiv2012

Collaborating Robotics Using Nature-Inspired Meta-Heuristics

This paper introduces collaborating robots which provide the possibility of enhanced task performance, high reliability and decreased. Collaborating-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration as aggregate Collaborating-bots can dynamically change their structure to match environmental variations. Collaborating robots are more than just networks of independent agents, they are potentially reconfigurable networks of communicating agents capable of coordinated sensing and interaction with the environment. Robots are going to be an important part of the future. Collaborating robots are limited in individual capability, but robots deployed in large numbers can represent a strong force similar to a colony of ants or swarm of bees. We present a mechanism for collaborating robots based on swarm intelligence such as Ant colony optimization and Particle swarm Optimization

cs.NE↗
arXiv2012

New SpiroPlanck Heuristics for High Energy Physics Networking and Future Internet Testbeds

The need for data intensive Grids, and advanced networks with high performance that support our science has made the High Energy Physics community a leading and a key co-developer of leading edge wide area networks. This paper gives an overview of the status for the world's research networks and major international links used by the high energy physics and other scientific communities, showing some Future Internet testbed architectures, scalability, geographic scope, and extension between networks. The resemblance between wireless sensor network and future internet network, especially in scale consideration as density and network coverage, inspires us to adopt the models of the former to the later. Then we test this assumption to see that this provides a concise working model. This paper collects some heuristics that we call them SpiroPlanck and employs them to model the coverage of dense networks. In this paper, we propose a framework for the operation of FI testbeds containing a test scenario, new representation and visualization techniques, and possible performance measures. Investigations show that it is very promising and could be seen as a good optimization

cs.NI↗
arXiv2012

Simulated Tom Thumb, the Rule Of Thumb for Autonomous Robots

For a mobile robot to be truly autonomous, it must solve the simultaneous localization and mapping (SLAM) problem. We develop a new metaheuristic algorithm called Simulated Tom Thumb (STT), based on the detailed adventure of the clever Tom Thumb and advances in researches relating to path planning based on potential functions. Investigations show that it is very promising and could be seen as an optimization of the powerful solution of SLAM with data association and learning capabilities. STT outperform JCBB. The performance is 100 % match.

cs.RO↗
arXiv2012

Go Heuristics for Coverage of Dense Wireless Sensor Networks

This paper collects heuristics of Go Game and employs them to achieve coverage of dense wireless sensor networks. In this paper, we propose an algorithm based on Go heuristics and validate it. Investigations show that it is very promising and could be seen as a good optimization. Keywords: Go Heuristics, Wireless Sensor Networks, Coverage, Density

cs.NI↗
arXiv2011

Iris Recognition Based on LBP and Combined LVQ Classifier

Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction, and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP is working on a grayscale level so colored iris images should be transformed into a grayscale level. The proposed system gives a high recognition rate 99.87 % on different iris datasets compared with other methods.

cs.CV↗