International Journal of Advanced Networking and Applications

4575-4584

Authors: Mubarak Albarka Umar, Chen Zhanfang

The growing need for quality software makes software testing a crucial stage in Software Development Lifecycle. There are many techniques of testing software, however, the choice of a technique to test a given software remains a major problem. Although, it is impossible to find all errors in software, selecting the right testing technique can determine the success or failure of a software testing project. Knowing these software testing techniques and their classification is a vital key in selecting the right technique(s). Software testing can broadly be classified as static or dynamic, this paper presents a broad comparative study of the various dynamic software testing techniques. An explanation of the dynamic testing techniques, their advantages and disadvantages, as well as some of the commonly used types of testing under each technique are presented. Finally, a comparison of the dynamic testing techniques is also made to enable a clear and definite understanding of the techniques for the betterment of testing and subsequent improvement in software quality.

4592-4595

Authors: Prakash Kanade, Monis Akhtar, Fortune David

Corona virus has plagued the world and has brought the life around the world to a standstill. The virus is highly transmissible, and with no vaccine or cure, the solution to it would be to follow strict quarantine. Governments from around the world have focused on the containment of the virus with varying degrees of success. Certain countries maintaining a low mortality rate are worth praise, and a detailed study of these efforts would benefit other countries to fight the virus. The use of technology and its integration into crucial strategies in fighting the deadly disease have proven beneficial on many fronts. Technology is being used to tackle unavoidable circumstances that may have arisen but put people at risk. Technologies that minimize human contact and can be remotely controlled reduce the risk of virus transmission from one another. The study aims to identify some remote technologies that have proved beneficial in the fight against the deadly Corona virus.

4601-4605

Authors: CS Reddy

Mobile ad hoc network (MANET) is a self-organized mobile nodes group that is connected fairly with low wireless bandwidth links. This manuscript proposes an innovative efficient queuing architecture, which supports for both elastic and inelastic traffic arises real time world. Packets or jobs of inelastic flows are always preferred for service comparing to those of the elastic flows. If any link is significantly weighted down by the inelastic traffic, it causes in great delays. To sort the problem the virtual queue algorithm gives the veteran delay compare to virtual queues that are served at a fraction of the actual service rate and by using the virtual queue-length values in utility function.

4613-4615

Authors: Pshwang Wang

With the continuous deepening of curriculum reform, the traditional education model has not met the needs of the new era. In the past, the traditional education mode, which attaches too much importance to academic achievements and neglects the development of students' comprehensive quality, is no longer suitable for the present teaching situation, and is not conducive to the cultivation of more interdisciplinary talents. Through the combination of online and offline teaching methods, students' learning enthusiasm can be fully aroused, their interest in learning can be stimulated, and their independent learning ability can be cultivated. This paper studies the hybrid teaching mode of online and offline for computer basic course in order to provide more references for improving the teaching effect.

4542-4548

Authors: Mona Nasr, Andrew karam, Mina Atef, Kirollos Boles, Kirollos Samir, Mario Raouf

In Data mining, Classification and prediction are the two very essential forms of data analysis. They are widely used for extracting models for describing important data classes. This paper aims in designing classifier models based on five different classification algorithms namely, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest and Support Vector Machines (SVM), to classify and predict patients with diabetes. These classifiers are experimented with 10 fold Cross Validation and their performances are evaluated by computing Accuracy, Precision, F-Score, Recall and ROC measure. The test experiment shows that the accuracy given by classifier models developed by using Decision Tree, KNN, Naïve Bayes, SVM and Random Forest are 73.82%, 71.65%, 76.30%, 65.10% and 68.74 % respectively. Similarly, their precisions and recall are 0.705, 0.552, 0.759, 0.424, 0.538 and 0.738, 0.763, 0.82, 0.651, 0.804 respectively. Thus, this study shows that the Naïve Bayes algorithm provides the better accuracy in predicting diabetes as compared to other techniques. And, the data set chosen for this study is “Pima Indian Diabetic Dataset” taken from University of California, Irvine (UCI) Repository of Machine Learning databases.

4555-4560

Authors: Thiraviaselvi G, Gnana Thiraviam G

Network Virtualization is one of the emerging fields of Research as it becomes pillar of the future Network. Here, Overlay Network Embedding (Virtual Network Embedding) is the major problem to be addressed to have optimized resource utilization. The main purpose of Overlay Network embedding is to efficiently utilize the physically networked resources to offer the supporting of virtual nodes. While most of the Overlay Network Embedding algorithms consider it as a mapping problem, Ruckpack++ algorithm uses cluster-based and greedy approach to select the optimized physical node. In the proposed algorithm, topology awareness is also included. Extensive simulations have shown that the proposed algorithm produces better Acceptance Ratio and node Utilization when compared to other state-of-the-art algorithms.

4567-4574

Authors: Ebrahem Said, Mona Nasr

One of the modern technological techniques in practical application will be discussed in this paper in broaden manner will be discussed on a large scale.This technique, which is considered a means of technological security to keep personal data away from the hands of snoopers and spies, this technology has occupied the minds of developers in recent years who have ensured its development continuously is a facial recognition technology.

4509-4518

Authors: Gajendra Sharma, UmeshHengaju

In Data mining, Classification and prediction are the two very essential forms of data analysis. They are widely used for extracting models for describing important data classes. This paper aims in designing classifier models based on five different classification algorithms namely, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest and Support Vector Machines (SVM), to classify and predict patients with diabetes. These classifiers are experimented with 10 fold Cross Validation and their performances are evaluated by computing Accuracy, Precision, F-Score, Recall and ROC measure. The test experiment shows that the accuracy given by classifier models developed by using Decision Tree, KNN, Naïve Bayes, SVM and Random Forest are 73.82%, 71.65%, 76.30%, 65.10% and 68.74 % respectively. Similarly, their precisions and recall are 0.705, 0.552, 0.759, 0.424, 0.538 and 0.738, 0.763, 0.82, 0.651, 0.804 respectively. Thus, this study shows that the Naïve Bayes algorithm provides the better accuracy in predicting diabetes as compared to other techniques. And, the data set chosen for this study is “Pima Indian Diabetic Dataset” taken from University of California, Irvine (UCI) Repository of Machine Learning databases.

4528-4531

Authors: Pshwang Wang, Zecheng Wang

In order to protect mobile social network users' privacy information, and promote the healthy development of the mobile social industry, This paper studies the types and ways of user privacy disclosure in mobile social networks, and proposes the privacy protection strategies for users in mobile social networks from the aspects of formulating national laws and regulations, building and managing mobile platforms, improving privacy protection technologies and enhancing personal protection awareness. It also briefly introduces the development trend and future research of access control policies in mobile social network.

4537-4541

Authors: Dr.K.R.R.MOHAN RAO, Dr K.KIRAN KUMAR, Prof. P.SUBBA RAO

In the fund world stock exchanging is one of the most significant exercises. Securities exchange expectation is a demonstration of attempting to decide the future estimation of a stock other money related instrument exchanged on a monetary trade. This paper clarifies the expectation of a stock utilizing Machine Learning[6]. The specialized and central or the time arrangement examination is utilized by the a large portion of the stockbrokers while making the stock forecasts. The programming language is utilized to anticipate the securities exchange utilizing AI is Python. Right now propose a Machine Learning[10] (ML) approach that will be prepared from the accessible stocks information and increase insight and afterward utilizes the gained information for a precise forecast. Right now study utilizes an AI system called Support Vector Machine (SVM)[1] to anticipate stock costs for the enormous and little capitalizations and in the three distinct markets, utilizing costs with both every day and regularly updated frequencies.

4429-4442

Authors: Vincent Omollo Nyangaresi, Anthony J. Rodrigues, Silvance O. Abeka

The motivations behind 5G networks include seamless handovers, higher data rates, lower latencies of about one millisecond, and enhanced coverage compared to 4G networks. To achieve these goals, network densification has been implemented to cope with increasing capacity demands. Networks with ultra-densification have large numbers of heterogeneous small cell deployments such as femto-cells, relays and microcells which complicate mobility management, resulting in unnecessary, frequent, and ping-pong handovers as UEs move within the network. To address these challenges, state of the art approaches using fuzzy logic, adaptive neuro-networks or their combination have been proposed. However, these approaches majorly address the QoS issues, ignoring the security aspect of handovers. In this paper, a handover protocol that incorporates both security and QoS in the handover process is proposed. The simulation results showed that this protocol reduced handover latency, packet losses, number of executed handovers and ping pong rate by 56.1%, 38.8 %, 74.6% and 24.1% respectively. In addition, the developed protocol yielded a 27.1% increase in the handover success rate, and a 27.3% reduction in handover failure rate. This protocol was also shown to be robust against de-synchronization and session hijacking attacks.

4451-4457

Authors: Mona Nasr, Omar Farouk, Ahmed Mohamedeen, Ali Elrafie, Marwan Bedeir, Ali Khaled

Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm’s result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.

4468-4474

Authors: 1 Yekini Nureni Asafe, 2 Oloyede Adetokunbo Olamide, 3 Akinwole Agnes Kikelomo

Computer and Internet-Based Communication Technologies aka Cybernetic Communication play important role in communication over a distance. This work gauged uses of cybernetic communication as major means of communication during COVID-19 outbreak based on measured (Social Distance, Self-Quarantine, Isolation, Lockdown) put in place by the government to curtail spreading of the disease in Nigeria. Data were randomly collected through online publication, newsprints, and telephone calls from 290 sampled population. Data Collected were discussed and analyses using Pie chart, Percentile, and Histogram as statistical tools. Findings shows that Cybernetic communication play major role to bridge the communication gaps between the Nigerian people during the outbreak of the Covid-19 outbreak as majority of Nigerians spent more hours using cybernetic tools to communicate and more money was spent on airtime and data to keep their mobile phone running during the trying period of COVID-19 rash. Majority of the sampled population spent their useful time on social medial as in Facebook, whattsapp, Instagram, YouTube and for social interactions and meetings. The implications of our findings are: majority of Nigeria may have computer linked diseases or syndrome such Sleeping Problems and others due to prolong uses of computers; the cybernetic tools (mobile phones, computer, laptop etc.) used by the infected people may be a carrier of the symptoms in the nearest future; Nigerians spent more on data and airtime credit in this trying period and it may affect their financial economy later. This work ends with proposal for roles to be played by Nigeria government and other stakeholders in cybernetic sustained communication processes for pandemic response and to prevent future outbreak of covid-19 after overcoming recent happenings, and also recommended organization strategy for workforce continuity and recovery.

4486-4493

Authors: Dr. M. N. Faruk, Dr. G. Lakshmi Vara Prasad, Dr. K. Lakshmi Prasad

Cloud computing is actually a developing ideal to provide on-demand IT services to end-users. The get accesses to command to information situated in the cloud is just one of the essential parts to enable service to change right into the cloud. Some latest works offer access management models ideal for the cloud; nonetheless, there are essential shortages that need to be resolved in this field. This work offers a progression in the state-of the-craft to get access to management for cloud processing. We illustrate a higher meaningful consent version that permits the management of state-of-the-art attributes such as role-based to get access to management (RBAC), hierarchical RBAC (hRBAC), relative RBAC (cRBAC) and also ordered objects (HO). The get access to management design takes benefit of the logic formalism delivered due to the Semantic Internet technologies to designate both the rooting framework and also the certification style, as well as the rules worked with to guard the get access to sources in the cloud. The access control style has actually been specially established taking into consideration the multi-tenancy model of this type of environment. Moreover, this rely on a style that allows a powdery meaning of what information is on call for every specific tenant has been actually illustrated. This multi-tenant endorsement model permits a fine-grained definition of information sets is being available for the individual tenants. Certainly this assures formation of business associations among cloud tenants resulting in confederation and association agreements. The suggested model has actually been confirmed via verification of concept implementation of the gain access to management device for OpenStack along with promising efficiency end results.

4501-4508

Authors: Mona Nasr, Rana Osama, Hussein Ayman, Nouran Mosaad, Nourhan Ebrahim, Adriana mounir

This paper explains how to detect the 2D pose of multiple people in an image. We use in this paper Part Affinity Fields for Part Association (It is non-parametric representation), Confidence Maps for Part Detection, Multi-Person Parsing using PAFs, Simultaneous Detection and Association, this method achieve high accuracy and performance regardless the number of people in the image. This architecture placed first within the inaugural COCO 2016 key points challenge. Also, this architecture exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

4378-4385

Authors: He Hong, Wang Ning

The research on group mobility in the opportunity network concerns what nodes’ mobility may bring about, in contrast to the preciously-proposed group mobility approaches that are unilateral in the thoughts of the constitution of groups by nodes, resulting in, the distortion of reality to reflect group behavior characteristics that are revealed in the situations of their application. Based on the Spectral Clustering algorithm, this paper proposes the group mobility model (SCM), which takes the advantage of node’s own properties, such as its spatial position, to set up node’s affinity matrix, extract main characteristics from arbitrarily distributed nodes with the aid of spectral clustering algorithm, and regarding the needs of a task assigned cluster nodes dynamically in a fixed time. The model depicts truly and effectively the group characteristics of node’s mobility, accomplish the conducts of group mobility and clustering well.

4393-4398

Authors: Tang Zhi-hang, Guo Tao, Li Jun, Wu Shi-qi

Use python web crawler to collect data from Trade website. The collected data is down jacket information. The fields are shell material, structure type, filling material, process information and style information. This information can be used for data mining, using clustering algorithms, correlation algorithms, etc. to identify potential value, providing decision-making reference for the management of textile and garment enterprises, with strong practical value. This paper provides a new idea for the development of textile and garment enterprises. The employees of the company screen, deal with the missing data and standardize the data, and then conduct data mining. The management of the enterprise makes decisions based on the results of data mining to improve decision-making basis and correctness.

4407-4417

Authors: Dr.Vijay Arputharaj J, Ms.Pushpa Rega Ganesan, Mr.Ponsuresh Manoharan, Ms.P.Supraja

Gene is a basic component of DNA located in the nucleus of Human cell. Currently data mining technique has huge impact in fields of human genetic science and gene sequence data analysis. Gene sequence analysis is a method of subjecting DNA sequence to systematic methods in order to know the genes character, configuration, nature and characteristics. CBC and MNBC applied to gene sequence data analysis, aims to segregate diseased diabetic genes from a vast stream of DNA gene sequence elements present in group of copious statistical data. This techniques attempts to approve, determine methods and tools for analyzing diseased gene sequences. It also helps in classification and interpretation of results accurately and meaningfully. This study is a combination of supervised and unsupervised machine learning technique for data analysis. The clustering is done by CBC whereas classification done by MNBC techniques. It recognizes gene expressions by framing association rules in accordance with support measure and confidence measure on the input data set.It will extract and filter required data into clusters based on CBC technique thereby drafting association rules. These are then applied on testing dataset to filter required (diseased) gene sequences. Finally MLRC algorithm is applied as classification algorithm to identify class labels of test genes sequences in a big dataset. In medical diagnosis gene data mining techniques through gene discretization models helps to identify various associations between the DNA genes based progressions and inconsistency in disease infections transformations. Above all it overcomes the limitation of existing Support Vector Machine Classification technology which incurs high computational cost and increased iterations.

4423-4428

Authors: NaglaaSaeedShehata, Amira Hassan Abed

Due to the Era of Big Data with the large amount of distributed databases in the web and the rapid growth in the smart systems a rapid growth happening in database models and the relational database fails to dealing with such a big amount of data and have many limitations the need to new technologies comes up, which makes DBMS developers move towards column oriented NOSQL database. The main goal of this paper is to provide a survey on NOSQL Model especially a column oriented NOSQL database, providing the user with the benefit of using NOSQL database, Instead of using the (row database) relational to overcome the drawbacks of the relational database Model.