big data infers in all processes in our life. But the analysis process of huge data is not sufficient, the human brain heads for detecting a pattern more efficiently when data is performed visually. Data Visualization and Data Analytics play a significant role in decision making in various sectors. It also opens new opportunities for the ideas for big data visualization domain. An essential challenge in data visualization is a huge amount of data in real time or in static form. This paper discusses the importance of data visualization. This paper illustrates the impact of big data on data visualization. It focuses on the explanation of the essential challenges of big data visualization in the real-time stream.

The recorded Electroencephalography (EEG) data comes with a large size due to the high sampling rate. Therefore, large space and more bandwidth are required for storing and transmitting the EEG data. Thus, preprocessing and compressing the EEG data is a very important part in order to transmit and store it efficiently with fewer bandwidth and less space. The objective of this paper is to develop an efficient system for EEG data compression. In this system, the recorded EEG data are firstly preprocessed in the preprocessing unit. Standardization and segmentation of EEG data are done in this unit. Then, the resulting EEG data are passed to the compression unite. The compression unit composes of a lossy compression algorithm followed by a lossless compression algorithm. The lossy compression algorithm transforms the randomness EEG data into data with high redundancy. Subsequently, A lossless compression algorithm is added to investigate the high redundancy of the resulting data to get high Compression Ratio (CR) without any additional loss. In this paper, the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are proposed as a lossy compression algorithms. Furthermore, Arithmetic Encoding and Run Length Encoding (RLE) are proposed as a lossless compression algorithms. We calculate the total compression and reconstruction time (T), Root Mean Square Error (RMSE), and CR in order to evaluate the proposed system. Simulation results show that adding RLE after the DCT algorithm gives the best performance in terms of compression ratio and complexity. Using the DCT as a lossy compression algorithm followed by the RLE as a lossless compression algorithm gives CR = 90% at RMSE = 0:14 and more than 95% of CR at RMSE = 0:2. Index Terms—EEG data compression, lossy Compression, lossless compression, DCT, DWT, RLE, arithmetic encoding.

Keeping confidential data and hiding it from unauthorized people has become a real challenge during this time, cryptography play a major role for secured data transfer. Quick Response Code (QR Code) are 2-dimensional barcodes that encode text strings. QR code can be used for various applications such as business, marketing, education, data security, authentication etc. They are able to encode information in both vertical and horizontal direction, thus able to encode more information. In this paper, a novel cryptography technique based on Quick Response code (QR code) is proposed, the technology encrypts secret message by using Advanced Encryption Standard algorithm (AES), then hide secret message in QR code, the QR code is saved as black and white image. Arnold transform was then applied to the Quick Response Code, which performs the scrambling operation by randomly changing the pixel positions of an QR code image, finally the scrambled image is then decomposed into five coefficients using the Shearlet transform, Shearlets have been applied to image for enhancement. Experimental result shows that the proposed method has more security and enhances the confidentiality. Keywords— QR code; AES algorithm; Arnold transform; Shearlet transform.

in recent years, there have been many problems in the import and export of medicines in Egypt. Which led to the deaths of many people, especially children due to infant milk deficiency. And the deterioration of the health status of a large number of chronic diseases due to lack of these drugs, whether tablets or injections. So we built this new system, which depends on the association of all pharmacies in each region. This system is based on incomplete medicines. We chose the blockchain method because it will help to buy any pharmacy from the other or direct the patient / client to it easily. Any pharmacy can put the drugs and put the amount they have on them, which ensures the availability of medicines constantly. Based on the statements of the Egyptian Ministry of Health, there are about 213,000 pharmacists in Egypt who have about 71,000 pharmacies serving approximately 100 million Egyptian citizens. Egypt contains the equivalent of 12000 medicines registered in the ministry and price. The proposed system can also be used to control drug scaling and dispensing to the consumer through a doctor's prescription. The technology of Blockchain relies on a mathematically guaranteed solution for cyber security to recognize the utility of digital data and smart devices while supporting the transparency and dependability to all communities and organizations cooperative with digital data or smart devices in the public or private sector.

Internet of Things is Kind of technology most prevalent and expansion which is growing rapidly in the field of communications that related with wireless telecommunications. Cloud computing that refers to share compu-ting resources rather than having local servers to handle applications data stor-age and processing. that achieved Con-venience, Scalability, Low costs, and High availability, now we discuss Cloud Computing and IoT, regard the field of wireless communications with security is-sues of both technologies. That provides cloud services and security architectures designed to support the IoT Organiza-tions can operate and manage multi-do-main IoT deployments across trust boundaries. Cloud security are big data aspects of the IoT that require security. That delve into IoT data storage, data an-alytics, reporting. and how to secure these services. Securing the various fac-ets of IoT in the cloud also requires to ad-dress what elements of security are the responsibilities of the customer versus the cloud provider.

Blockchain is distributed database of records, or public ledger of all transactions or digital events that have been executed and shared among participating parties. Each transaction in the public ledger is verified by consensus of a majority of the participants in the system. Once entered, information can never be erased. Blockchain is the core mechanism in bitcoin. Bitcoin that is often called the first crypto currency has enjoyed a huge success with the capital market. Nowadays, crypto currency has attracted attentions from both industry and research projects at top universities. The main features in blockchain are decentralization, persistency, anonymity and auditability. It can be applied into many fields, including financial services, reputation system, and public services. It is important to present a large number of technical challenges prohibiting the wide application of blockchain such as scalability problem, privacy leakage and others. This paper will present a comprehensive survey on the state-of-the-art research efforts on blockchain technology, applications, technical challenges, and future research directions will be covered. Also talk it is necessary to cover open research problems for Egypt in order to make our country to be one of the leading countries in this area and to be in the top of 30 globally

Cloud Computing is known to use common software, infrastructure, virtual machines (VMs), and other resources on the cloud as services. It is a utility computing model in which the customer can get a required infrastructure without buying it. The customers only want to pay for what they use. The scheduling on cloud computing is a strategy or method used to define the most appropriate deployment for the available tasks, resources or jobs. So that there are many available distinct cloud scheduling algorithms which coordinate between the tasks and the appropriate resources to get the best and most efficient way of resources use with regards to some measurement factors to get the minimum value of time, minimum value of cost, minimum value of delay, or to maximize the resources utilization. In this paper, we propose a cloud scheduling technique that works as a strategy of three stages. In the first stage, a job classifier is used for task classification. Which helps to pre-create a different type of virtual machines. And save the time needed to create virtual machines during the scheduling process also decrease the task scheduling failure rate. In the second stage, we sort the tasks in an ascending order depending on the priority of the length then mention the state of the virtual machine that satisfies the deadline constraint as successful. In the third stage match the tasks dynamically to their corresponding virtual machines with the minimum completion time and compare the algorithm with the Min-Min algorithm and Max-Min algorithm. The comparison depends on some evaluation factors like the makespan, average waiting time, the task scheduling failure rate and the virtual machine utilization rate that will improve the scheduling process performance and the cloud resources load balancing.

This paper studies the problem of simulating a range-based localization algorithm using NS-2. Because of NS-2 is not ready for simulating a localization algorithms, this area has been gained researchers interest. So that we present the approach of how to extend NS-2 in order to simulate a range-based localization algorithm called multilateration localization. Extending NS-2 is done by adding new modules which help researchers to modify existing libraries. Furthermore a comparative study of two kinds of multilateration localization is done according to different performance metrics.

Owing to the explosive growth of requirements of rapid emergency communication response and accurate observation services, airborne communication networks (ACNs) have received much attention from both industry and academia. ACNs are subject to heterogeneous networks that are engineered to utilize satellites, High Altitude Platforms (HAPs), and Low Altitude Platforms (LAPs) to build communication access platforms. Compared to terrestrial wireless networks, ACNs are characterized by frequently changed network topologies and more vulnerable communication connections. Furthermore, ACNs have the demand of the seamless integration of heterogeneous networks such that the network Quality of Service (QoS) can be improved. Thus, designing mechanisms and protocols for ACNs poses many challenges. To solve these challenges, extensive research has been conducted. This paper also points out future research directions and challenges in the field of ACNs, LAP based communication networks, HAP based communication networks, and integrated ACNs.

— Network attacks are one of the most common dangers user face these days all over the World. Serious attacks such as denial of service (DoS) attack may lead to the full interruption of the service to the entire users. A distributed denial of service (DDoS) attack is more sophisticated attack that is harder to be detected and can cause a brutal damage on the system. Detection of these dangerous known networks can be done by exploring the packet information. Typically, packets are analyzed by detection system to help the identification of the attack type. Therefore, artificial intelligent methods are used to improve the diagnosis of these attacks. But it is very challenging, since the accuracy of the detection system is still an issue. Artificial neural networks (ANN) is used to improve the the detection accuracy by providing a comprehensive analysis on the users received packets. One of the important factors that impact the detection system performance is the true positive ratio to classify the packets. In this paper, a modified detection method is introduced based on ANN. The results below show that the proposed modified method achieve a better performance compared to the existing ones. A very high accuracy was achieved using the proposed method when the method is applied on the chose a range of NSL-KDD dataset[1]. Performance f the proposed method is analyzed and the system is concluded with the future work outlined.

The recorded Electroencephalography (EEG) data comes with a large size due to the high sampling rate. Therefore, large space and more bandwidth are required for storing and transmitting the EEG data. Thus, preprocessing and compressing the EEG data is a very important part in order to transmit and store it efficiently with fewer bandwidth and less space. The objective of this paper is to develop an efficient system for EEG data compression. In this system, the recorded EEG data are firstly preprocessed in the preprocessing unit. Standardization and segmentation of EEG data are done in this unit. Then, the resulting EEG data are passed to the compression unite. The compression unit composes of a lossy compression algorithm followed by a lossless compression algorithm. The lossy compression algorithm transforms the randomness EEG data into data with high redundancy. Subsequently, A lossless compression algorithm is added to investigate the high redundancy of the resulting data to get high Compression Ratio (CR) without any additional loss. In this paper, the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are proposed as a lossy compression algorithms. Furthermore, Arithmetic Encoding and Run Length Encoding (RLE) are proposed as a lossless compression algorithms. We calculate the total compression and reconstruction time (T), Root Mean Square Error (RMSE), and CR in order to evaluate the proposed system. Simulation results show that adding RLE after the DCT algorithm gives the best performance in terms of compression ratio and complexity. Using the DCT as a lossy compression algorithm followed by the RLE as a lossless compression algorithm gives CR = 90% at RMSE = 0:14 and more than 95% of CR at RMSE = 0:2. Index Terms—EEG data compression, lossy Compression, lossless compression, DCT, DWT, RLE, arithmetic encoding.

The multiple sequence alignment is an open problem in the bioinformatics field, because all of the current solutions are still suffering from strong limitations. One of these limitations is a low performance of current solutions. In this paper, a algorithm called BM2Align is developed for improving the current solutions in terms of the execution time and memory allocation. This algorithm is specifically improving the standard encoding scheme in the standard toolkit called M2Align by using the bit map index strategy that reduces the memory allocations, which in turns reduces the execution time as well. The paper also validates the developed algorithm using core systems ranging from one core to four cores and the standard Bali-base data set as a benchmark. Our experimental results improve considerably the reported results of the original algorithm called M2Align. Indeed, the M2Align algorithm recently developed is compared with current algorithms in the literature and it outperforms than these algorithms in terms of the execution time and memory allocation.

A key factor in the success of cloud computing is the ability to obtain optimal VM configuration quickly enough to adapt to sudden workload changes. When queueing is used in clouds a scheduling technique, which is often the case to ensure fairness and efficiency, a methodology is needed to assess the queueing system metrics, such as the expected system occupancy and expected request waiting time. In this article, we model the cloud queueing environment as a Geo/Geo/c system. This system has an exact solution, but it suffers from being extremely cumbersome, especially when the number of servers is high. The solution even becomes almost impossible when c is in the thousands, which is unfortunately the case in clouds. As such an alternative is needed, which is provided by the formula we derive in this article. The formula, being fast, can therefore be crucial for VM provisioning in response to urgent and abrupt change in workload. Index Terms—Cloud computing, Data center, Multiserver Queue, Quality of Service, System Occupancy, Waiting time.

This paper presents an improved approach to detect and classify the reflected signals from buried landmine at different depths based on deep learning technique. The reflected signals are obtained by Ground penetrating radar (GPR) technique to get the signal as a signature of the target. The convolution neural network (CNN) is the most important acceptable algorithms of the deep learning architecture; it is good used for processing one-dimension (1D) data of raw signals to extract useful features automatically. The reflected signals generated from buried landmine in the soil conditions at different depths includes 1, 3, 7, 12 and 15cm, as well as detecting of doesn’t the absence of landmine. This paper takes in its consideration the unwanted signals caused by nearby non-landmine objects and the secondary reflections, to detect and classify the landmine depth. The proposed model structure based on deep learning showed that the proposed model recorded high accuracy of detection and classification for the noisy and noiseless landmine signals at different depths.

Breast cancer is one of the most common malignancies among women in the world, which may lead to death. Survivability rate of breast cancer can be improved if it is identified in its early stage. Breast thermography plays an important role in early detection of breast cancer, since it couples the physiological information with the anatomical features of woman breast. Typically, thermograms are visually analyzed by physicians for breast cancer early diagnosis. But it is very challenging, since it is hard to provide objective and quantitative analysis. Therefore, Computer Aided Detection (CAD) systems are used to improve the diagnostic accuracy by providing a comprehensive analysis on these Thermograms. One of the important factors that impact CAD system’s performance is the classifier used for the classification of breast thermograms. However, problems such as low rate of accuracy and poor self-adaptability still exist in traditional classifiers. In this paper, a hybrid approach consists of support vector machine (SVM) classifier, with feature selection and boosting ensemble method, is proposed to enhance the performance of SVM classifier. AdaBoost algorithm is used as our boosting ensemble method. The experimental results show that the proposed hybrid approach achieves a better performance compared to the base classifier SVM alone and the base SVM classifier coupled with feature selection method. An accuracy of 99.24% is obtained using our hybrid approach.

Micro-Electro-Mechanical systems (MEMS) are widely used in applications ranging from consumer electronics to aerospace. Gyroscopes are commonly used sensors for measuring angular velocities in many areas of applications such as homing missiles, Unmanned Ariel Vehicles (UAVs), and stabilized platforms. On the other hand to properly design autopilots of UAVs; a six degree of freedoms (6DOF) simulation model is usually built, and to accurately tune the autopilot parameters the feedback sensors should be well modeled. Toward that, this work is intended to accurately model a low cost MEMS gyroscope. The angular rate tests used for calibrating the biases, scale factors, and non-orthogonal ties of gyroscope of (MPU6050 from Invensense) used as a case study. The linearization of the designed gyro model is required for designing and analysis of linear control system. The validation of the designed simulated model with the real raw data of the MEMS gyroscope is given which confirms the accuracy of the simulated model.

Multiple protein sequences alignment (MPSA) is a fundamental problem in bioinformatics. MPSA aim to discover the similarity relationship between multiple protein sequences. Standard dynamic programming algorithm (DP) compute the optimal alignment accuracy, but DP is more consuming time for large scale sequences. The existing methods implemented DP with divide and conquer (DAC) algorithm, but existing methods are not good enough, because of partitions are fixed to the middle or near middle points and not efficient for large scale sequences. In this paper, a new method for MPSA based on dynamic programming is proposed. Proposed algorithm choose the good cutting point for multiple protein sequences based on LCS strategy which cutting point is very critical for accuracy. Experimental results show that the proposed algorithm improves accuracy and reduce the running time than using DAC algorithm or without partitions

Recently, there are emergence and advent of data Inter-personal interaction web sites, micro blogs, wikis, in addition to Web applications and data, e.g. tweets and web-postings express views and opinions on different topics, issues and events in many applications, in addition to, different domains that includes business, economy, politics, sociology, and etc., which are resulted from offering immense opportunities for studying and analyzing human views and sentiment. The objective of sentiment analysis is to classify a speaker's or a writer’s attitude towards various events or topics and arranging data into positive, negative or neutral categories. Sentiment analysis means determining the views of a user from the textual content regarding that topic i.e. how one feels about it. It might be used to classify the text content. Various researchers have used a widespread sort of methods to teach the classifiers for the Twitter dataset with various results. The research uses a hybrid method of using Swarm Intelligence optimization algorithms with classifiers. For each tweet, pre-processing will be done by performing various processes i.e. Tokenization; removal of stop-words and emoticons; stemming. Then their feature vectors are being made by the calculation of TF-IDF and optimized with (PSO) and (ACO) before performing the binary text categorization. Naïve Bayes and Support Vector Machine may be those machine learning technicalities used for the binary classification of tweets.

One of the most term in the biometric technologies is the voice recognition. Speech recognition is a complicate problem .But in today's society when technology is consistently striving for voice driven implementation, speech recognition can be a very useful system .This paper aims to build a system of voice recognition using two systems one of them is based on Fourier descriptors of speech and the other is Artificial neural network which is using back propagation algorithm in neural networks, The first speech recognition system is using Fourier descriptor of speech to get four formants of any speech signal. The second intelligent system is built neural network which is trained and tested using data sits of formants of speech signals. After we applied two systems for speech, the first method is not accurate because it depends on the human element in comparison and identification. This method makes it possible to have the error ratio greater than the second method, which is smart and accurate and depends on the intelligent system stable and continuous and has no life As well as not influenced by the external influences that might control the decision in the first method.

Consider a robot which is navigating in a space modeled by a graph, and which wants to know its current location. It can send a signal to find out how far it is from each among a set of fixed landmarks. We study the problem of computing the minimum number of landmarks required, and where they should be placed, such that the robot can always determine its location. The set of nodes where the landmarks are placed is called the metric basis of the graph, and the number of landmarks is called the metric dimension of the graph. The metric dimension of a graph G is the smallest size of a set R of vertices that can distinguish each vertex pair of G by the shortest-path distance to some vertex in R. It is well known that the problem of finding the metric dimension of a graph is NP-hard. In this paper we determine the metric dimension of mirror graph, path related graph, Tortoise graph, Z-(Pn) graph and middle graph. Keywords— Distance, metric dimension; mirror graph; Tortoise graph; middle graph

In the supply chain management area, with the rapid growth of the internet of things, many kinds of information systems have been designed and applied for improving the safety of food products. However, all of these information systems are centralized systems which have many weaknesses. Our research problem is that this kind of centralized system is a monopolistic, asymmetric and opaque information system, which could result in the trust problem, such as fraud, corruption, tampering and falsifying information. Besides that, there are some other problems, such as incomplete and duplicate information, which can seriously reduce the efficiency of the food supply chain management. Thanks to the internet of things and the block chain technologies, these kinds of problems can be solved. To improve the loop of Supply Chain Management loop manufacturing using block chain & IoT to help customers know the process of manufacturing within Egypt Air-Flight Services to ensure transparency and customer loyalty. In this paper, we discuss how to improve the supply chain quality management by adopting the Blockchain and IoT technology, and propose a framework for Blockchain-IoT based supply chain quality management. Food safety concerns have drawn much attention from researchers in many different domains. Blockchain & IoT are used in inventing an algorithm to enhance the quality and safety of food. This is needed to improve the efficiency and effectiveness of the food supply chain management. Since there is no automated system at Egypt air-Flight Services Company foods supply chain, which made things there uncontrollable. Therefore, we thought of automating this part in them.

Cloud computing has emerged as an alternative to traditional computing. It enables users to gain access to huge computing resources in an economical and efficient fashion. Cloud Computing is built on top of several other technologies, for example Distributed Computing, Grid Computing, and Utility Computing, General disadvantages of cloud computing are dependency upon network connectivity, security because of third-party cloud service provider , legal issues(ownership of data), latency , availability etc. which needs to be carefully reviewed. Digital library management system provide a key technologies for the storage, preservation and dissemination of knowledge in its various forms, This paper aims to reviewed the difference approaches for building secure and scalable digital library management systems based on cloud computing, It also summarizes and analysis of various issues related to data security of cloud computing, and discuss the issues, challenges, and benefits of cloud computing . Keywords— cloud computing, Digital library, security, threats, confidentiality, integrity, availability.

Nations that haven't taken measures to prevent threats to their interests and national security calling for such threats to undermine them. The truth is that there is no country in the world far from the dangers of crises and disasters. The crises and disasters have accompanied man since his presence on the surface of the earth and dealt with it according to its possibilities and tools to reduce its effects. In this paper we made a simplified survey about the crisis management and the role of the information technology in the crisis management to use the maximum possible capabilities in the field of information and communication technology to support the decision of crisis centers and upgrade them to overcome the various disasters.

One of the essential advantages of e-government implementation is the enhancement of transparency among public sector organizations. This transparency could be deduced through the availability of the information, accountability and the ability to track and monitor the transactions within the public sector. This research will pay attention to the problem of unemployment among postgraduates in Saudi Arabia. It attends to reap the benefit of the centralized and high efficient e-government system to control the process of employment in the universities and research centers in Saudi Arabia. One of the main objectives of vision 2030 is to increase employment and insure equal access to job opportunities through fostering the transparency among the public sector organizations. Although, Saudi Arabia launched the largest scholarship program in the history with more than 60,000 students in over 30 countries worldwide with ambitious objectives for its graduates, the country did not reap the full benefits of this program due to the low level of transparency among the public sector which consider as an essential challenge faced the postgraduate student in order to fill the gap and achieved their dream to contribute to the country development view. The idea of this research is to provide the postgraduates equal opportunity to compete for the jobs in theses government organizations through e-recruitment systems. This process is subject to external auditing and control for high transparency. Current corruption risks can appear because of lack of supervision and provide full privilege to the organization to select their employees without clear criteria and auditing. The proposed solution claimed that E-government and other ICTs can be employed to fight corruption via the supplying of information and qualifications of the job seeker in centralized websites. This proposition will allow the process of filtering and selecting by the employer to be supervised and audited. Key words: e-government, transparency, unemployment, postgraduates, erecruitment

Liver tumors are widespread in both worldwide. One of the most common types of tumors is the hepatic tumors. It occurs usually in chronic liver diseased hepatic patients. Tumor ablation can be used for treatment by destroying the liver tumor tissue/ cells without surgical removing procedure. Microwave (MW) ablation is an active search area. Currently, several studies are directed to developing new types of antennas with superior performance along with studying the thermal effect of the MW probe. This work is concerned with the study of MW ablation using two types of antennas, namely the Triaxial microwave coaxial antenna (MCA) and floating sleeve dipole MCA at three different frequencies, namely 0.4433, 0.915, and 2.45 GHz. The ablation is applied to a tumor inside the human liver with difference geometrical diameters of the tumor. The finite element modeling (FEM) was used to study the amount of the required power by each antenna for the entire ablation of the tumor and the maximum temperature irradiated by each antenna according to the power used to feed.

Worldwide cancer is widespread between individuals that may occur at different locations, such as liver, kidney, lung or breast. To prevent the spread of the cancer tissues in different organs, the cancer tissues must be killed by raising their temperature to a specific value, hence their properties will change to a specific value to be ablated or treated. In this work, a computational study of radio-frequency ablation (RFA) using three-dimensional finite element method (FEM) of cancer was conducted to ablate the tumor regions according radio- frequency signal. Ionic agitation was performed, where the tissue ions are agitated by applying an alternating electric current through probe that has 4 radio frequency (RF) electrodes. The simulation results established that the ionic agitation results in ion friction, which in turn caused heat production. The volume of the ablated region was computed. Moreover, the maximum temperature through the model was measured in terms of the revolution angle of the electrodes in 2 cases, namely tissue only exists in the model, and tumor only exists in the simulation model. The simulation results depicted that the ablation volume through tissue only model is greater than tumor only model. Thus, an increase in the revolution angle of the electrodes is required to ablate larger tumor volume.

For medical images storage and transmission, compression methods have a significant role due to the medical images large size along with the importance to save the fine details. In traditional lossy compression techniques, a lot of important details may be lost. However, the compressed sensing (CS) helps to transmit the images fast with saving their details.In the present work, different CS methods were studies to improve the images’ recovery and Quality of service (QOS). Aweighted based CS was proposed using threshold weighting matrixfrom the discrete cosine transform of the medical image using convex optimization algorithm and compare it with traditional CVX and other two types

Breast cancer is one of the leading cancers for women when compared to all other cancers. It is the second most common cause of cancer death in women. The early detection and treatment of breast cancer can greatly improve the survival rate of patients. In this paper, the aim is to improve predictive performance by presenting a new hybrid classifier through the combination of support vector machine SVM and the grey wolf optimization GWO. We analyze the breast Cancer data available from the Wisconsin dataset from UCI machine learning with the aim of developing accurate prediction models for breast cancer using these hybrid methods. The breast cancer data with a total 569 rows and 32 columns will be used to train and test, by using hybrid methods namely SVM and GWO and achieve accuracy better than using SVM only with the same data. The experimental results, analysis and statistical tests prove the ability of the proposed approach to improve prediction performance against SVM.

The flexible mind of children can simply be carve and subjected to turn towards plenty of criminal activities. The criminal offence by children of age below 18 years or Juvenile delinquency is spreading in India. The criminal idea of the youth is contributed by diverse risk factors like family & parents, education, financial problems, peer groups, mental issues, drugs, media, etc. In this paper, two major risk factors of juveniles involved in crimes, the family background and education levels are analyzed with association rule mining methodology of data mining with juvenile crime dataset of India. The experiment results are used to make an appraisal of the association of two major risk factors, family background and education levels in juvenile delinquency in India

Maximum Power Point Tracking (MPPT) is very important for increasing the output power from PV arrays, and maximizing the efficiency of the extracted power. Extracting maximum power from the PV system relies on the control of Isolated Gate Bipolar Transistor (IGBT) switch boost converter. The techniques studied in this paper are: Perturb and Observe (P&O), Incremental Conductance (INC), Modified Direct Incremental Conductance (MDIC), PID controller, FOPID controllers, Open Circuit Voltage (OCV), Short Circuit Current (SCI) and Fuzzy Logic Control (FLC). The control algorithms are simulated via Simulink/ MATLAB package for comparing the performances in terms of Settling Time , Maximum Over Shoot (MOS) of the used techniques, the number of sensors needed for each technique, and tracking speed and accuracy. The simulation results show that the FLC technique has advantages for use in the MPPT problem, as it provides fast response under changes in environmental conditions such as radiation and temperature.

Service providers rely on different technologies such as Broadband and Fiber to the Home (FTTH) to provide their services such as Internet, TV, and Voice to customers. FTTH enables the providing high bandwidth for each customer which allows multiple services to be provided. The parentage of FTTH subscribers represented the highest among the other technologies and it is highly expected to increase according to the forecasting studies as referred in [1]. Using of FTTH service has a promising future since it facilitates a strong telecommunication network that avail different services to be provided to inhabitants. However, this service confronts several challenges, particularly in low population density compounds. This paper applies an assessment using Porter’s five forces framework [2] on the FTTH services to define the needed controls to solve the existing challenges. Porter’s model indicated that there the decision the developer to be a licensed operator has constraints: Financial constraints indicated the need to consider the number of units inside the project and it is shown by the financial cost analysis that starting from the projects with 1500 units that the operating cost begins to be able to be accepted and the growing in the value of operating cost after 1500 units begins to be low. The technical constraints indicated that it will be expensive for the developer to avail technical resources to meet the regulations requirements to be licensed. Finally, developing revenue streams and considering the customer experience due to the commitment of the developer to their customer is an important role the have an impact of the decision of the developer

Inter-satellite optical wireless communication systems (Is-OWC) is a technique that used to connect between two satellites. One of them is the transmitter and the other is the receiver. The medium between them is a free space. Is-OWC using at the ultra-high bit rate. In this paper, the proposed system using optical sub-carrier multiplexing (SCM) with four frequencies channel 3.5, 7.2, 10.8, 14.4 GHz respectively. This system is designed at distance 5000 Km, bit rate 4x1.8 Gbps, wavelength 1550 nm and input power 3 dB. The proposed system utilized electrical Phase Shift Keying (PSK) modulation technique for the design of Is-OWC system due to it widely used for the wireless communication system. PSK is a type of modification that changes the phase angle by 180° at each change of digital information from zero to one or via verse with the installation of the capacitance and frequency value. This type of frequency is less susceptible to transmission errors from interferences, noise, and dispersion of signal compared to other types. The quality factor (Q-Factor) and bit error rate (BER) are measured by using the eye diagram analyzer. The oscilloscope visualizer allows the user to calculate and display electrical signals in the time domain and it can display the signal amplitude and autocorrelation. Radio frequency (RF) spectrum analyzer allows the user to calculate and display electrical signals in the frequency domain and it can display the signal intensity, power spectral density, and phase. The results show that the output for the signal-to-noise ratio (SNR), the Q-factor and BER are improved more than the system before modelled which are shown at the eye diagram analyzers. The commercial optical system software called Opti-system is used to simulate and model The Is-OWC system. Keywords— Is-OWC, SCM, PSK, Q-Factor, BER, SN

in recent years, cloud robotics have been used is as an alternative to traditional networked robots or traditional Multi-Robot Systems (MRS) and have grown as a collaboration between cloud computing and robotic systems through advances in hardware resources, wireless networks, and the widespread presence of internet resources. A large number of robotic applications have been implemented based on cloud computing models by offloading the massive computation and a large amount of data to remote clouds. In this paper, a comparative study between a traditional HDec-POSMDPs MRS exploration and fire searching model and cloud-based HDec-POSMDPs MRS exploration and fire searching model is performed for different environments and different MRS team sizes, and a set of performance metrics have been used in the evaluation process such as meantime, number of movements, number of turns, and average energy consumption by a robot team to accomplish the task. The obtained results demonstrate that cloud-based HDec-POSMDPs MRS exploration and fire searching outperforms the traditional HDec-POSMDPs MRS exploration and fire searching model. Index Terms—Cloud-robotics, Multi-robot Systems, internet of things IoT.

Mobile cloud computing (MCC) is a combination of cloud and mobile networks that provide additional benefits for mobile customers, network operators, and cloud service providers. This paper covers the difficult security issue with MCC technology, especially in the authentication process during handover. Most current vertical handover authentication protocols still have some drawbacks, for example, undesirable delays in real-time applications, replay attack, and the man in the middle attack. In this paper, a new authentication protocol for heterogeneous networks in mobile cloud environment is proposed. The proposed protocol is depends on the understanding of the 3GPP Access Network Discovery and Selection Function (ANDSF), which uses the capacities granted by the IEEE 802.11 and the 3GPP Long Term Evolution– Advanced (LTE-A) interconnection. The proposed handover authentication protocol has exceeded the current protocols in terms of powerful security, key confidentiality, and efficiency that are used to reduce bandwidth consumption. Keywords—Mobile Cloud Computing; Handover authenticating; ANDSF.

All optical wavelength conversion a very important technique that adds benefit to optical communication network, such as flexibility and simplicity. Nonlinearity of some component, such as semiconductor optical amplifiers (SOAs), will help in converting wavelength of input signal to another new wavelength. This paper examines the performance of the two important SOA wavelength converters, based Cross phase modulation (XPM) and Cross gain modulation (XGM), and suggest a new proposal model combine the features of XPM, and XGM therefore minimize the restriction of each types. The enhanced series effects XPM and XGM, increase the extinction ratio (ER), conversion efficiency, the Quality factor of generated signal, consequently minimized the bit error rates (BER) more than an individual effect XPM or XGM. All performances are analyzed in terms of shifted wavelength, conversion efficiency, Quality factor, converted signal power and data rate 10 Gb/s. All simulations in this study are performed by optisystem ver. 7.

Cloud Computing is known to use common software, infrastructure, virtual machines (VMs), and other resources on the cloud as services. It is a utility computing model in which the customer can get a required infrastructure without buying it. The customers only want to pay for what they use. The scheduling on cloud computing is a strategy or method used to define the most appropriate deployment for the available tasks, resources or jobs. So that there are many available distinct cloud scheduling algorithms which coordinate between the tasks and the appropriate resources to get the best and most efficient way of resources use with regards to some measurement factors to get the minimum value of time, minimum value of cost, minimum value of delay, or to maximize the resources utilization. In this paper, we propose a cloud scheduling technique that works as a strategy of three stages. In the first stage, a job classifier is used for task classification. Which helps to pre-create a different type of virtual machines. And save the time needed to create virtual machines during the scheduling process also decrease the task scheduling failure rate. In the second stage, we sort the tasks in an ascending order depending on the priority of the length then mention the state of the virtual machine that satisfies the deadline constraint as successful. In the third stage match the tasks dynamically to their corresponding virtual machines with the minimum completion time and compare the algorithm with the Min-Min algorithm and Max-Min algorithm. The comparison depends on some evaluation factors like the makespan, average waiting time, the task scheduling failure rate and the virtual machine utilization rate that will improve the scheduling process performance and the cloud resources load balancing.

In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.

Voice over IP (VoIP) technology increases performance and capacities of telephone service for business and individuals around the world. VoIP depends on the use of packets, which permit of carrying more information over the network to meet communication needs. This technology excreted some challenges such as security, scalability, and reliability to handle such immense data exchange over the internet. Furthermore, the use of cloud computing adds another aspect to these problems concerning of the load balancing of calls distribution through the virtual machines VMs due to the repeated use of Internet Protocol shortest path towards the same destination. In this paper, a deep survey of the most popular methods and techniques of VoIP load balancing over cloud is provided. We proposed a model depends on the dynamic load balancing for allocating calls to the destination by measuring the utilization of the servers and assign theses requests to the least utilized VM to ensure least delay and packet loss and best performance. We installed the proposed model using 3CX over Cloud. NS2, a simulation tool used to generate VoIP traffic and comparison between different Load balancing policies.

Long time ago, the information technology is penetrated through the industry and industry applications. It is the main part in the sensors and actuators, controllers, monitoring, data acquisition, industrial networks, management, and decision making. For the fabrication itself, the information technology regroups all the manufacturing activities in one computer system, called Manufacturing Execution System (MES). In this paper, the state of the art of MES is exhibited, based on the American National Standard Institute (ANSI), ISA-S95 standard. The MES application is implemented on the shaping of thermoplastic material by the Plastic Injection Molding Machine (PIMM). The Artificial Intelligence (AI) is used to implement the expertise of the: (i) producers of thermoplastics, (ii) the producers of the PIMM to control the process parameters, and (iii) the production engineers of the plastic shaping factory. Also, the AI is implemented for the quality control of the products. The MES localization is taken into account for the labors and technicians in Arabic countries. All the Graphical User Interface (GUI) are written in Arabic technical terms (which are familiar for the labors and technicians). The MES is implemented and tested in AL ARABY Group of Plastics Factories.

This paper presents two efficient approaches for radiation effect identification using deep learning and image processing approach for the classification of images transmitted over optical fibers. The first approach discovers the effect of gamma radiation on optical fibers from certain images with certain repetitive patterns. The quality of the images obtained can be used as an indicator of the radiation effect. A deep learning model is built for this objective comprising 6 convolution layers followed by 6 max pooling layers. The other approach depends on the analysis of zebra chart image based on depth/edge model. Quality metrics of the received images are used for classification on of the cases with and without radiation. Simulation results reveal high efficiency of the two proposed approaches for radiation effect detection.

Gamma radiation influences the optical fiber characteristics. This influence leads to chromatic dispersion. This work presents two efficient approaches for radiation effect identification using neural modeling and signal processing. The neural modeling of optical fiber transmission bit rate under the effect of gamma radiation to be used instead of solving complicated time-domain differential equations at every time for performance characterization of optical fibers .Also, describes the chromatic dispersion in optical fibers based on an Artificial Neural Network (ANN) to obtain a good arrangement measured and model values. The other approach depends on k-means clustering performed on spectrometer counts versus wavelength The Levenberg–Marquardt algorithm is used for the optimization of the used neural network. Simulation results reveal high efficiency of the two proposed approaches .the ANN model estimated can produce accurate outputs compatible with the measured values. Additionally, the results reveal that the proposed technique reduces the process time required to solve complicated equations in every case.

Let G = (V, E) be a connected graph and d(x, y) be the distance between the vertices x and y in G. A set of vertices W resolves a graph G if every vertex is uniquely determined by its vector of distances to the vertices in W. A metric dimension of G is the minimum cardinality of a resolving set of G and is denoted by dim(G). In this paper we determine the metric dimension of starting by Borromean link graph, the series of link graph starting by Whitehead and the third power of a path Pn .