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Throughout the progress of technology at Kurdistan Region Independent (KRI) Universities, it was investigated the influence of technological resources on teaching and learning at the KRI Universities. The all modifications of technological resources occurred from past up to now which created a new teaching and learning generation. Before year 2003 the traditional approach was applied in teaching and learning without any improvement due to the sanctions over Kurdistan Region by Ba`ath Regime. As a result of that, the universities at Kurdistan were isolated from the universal development of technology. Respectively, traditional teaching (TT) approach (or On-campus teaching) was more common at the University of Sulaimani as teachers had limited materials that they relied mainly on the suitable books for the course, face to face, and (whiteboard-and-marker) teaching. The new generation, namely electronic teaching (ET) (or Off-campus teaching) started after year 2003 because the University of Sulaimani did not go through all the stages of developing technology, therefore, most modern technology were brought to that university; for example network, computer and internet. However there is still a gap between TT and ET because of unavailability of electronic learning (e-learning) infrastructure or any e-learning (EL) center at that university so far. Despite that, the web base teaching (WBT) has not been functioned yet and that is turn out to be a gap between teachers, learner and technologies; in addition to that teachers and learners have been using technologies for the reason of teaching but they don’t have sufficient background about the fully e-teaching (ET). This paper discussed the function of modern technological devices components (computer, internet and networking) to teaching approach and the method that has been created here is a web base teaching (WBT), with multimedia devices by lecturing video synchronized with PowerPoint which contains of twenty five week- lectures loaded to specific domain name and students can retrieve whenever they want. Finally, this paper examines the results of In-class-Test between groups of ten students in Computer science Department which they will have test by both of the methods namely on & Off-campus-teaching. Subsequently the lecturer investigates which result is more attractive by the teachers and students. The results show either in traditional or on the Web and ways or utilizing the computer and internet with strong network infrastructure for improving teaching in Sulamani University. The use of software application Camtasia studio8 for PowerPoint slides with teacher voice combined. The investigation of the real time for all both ON&OFF campus by testing groups of students and compare the results between the results.

E-learning and e-teaching; based-Computer teaching; web base teaching; On and Off campus teaching

[1] I. F. Akyildiz, X. Wang, and W. Wang, “Wireless Mesh Networks: A Survey”, Computer Networks and ISDN Systems, Vol.47, Issue-2, 2005, pp.445-487.
[2] I. F. Akyildiz, and X. Wang, “A Survey on Wireless Mesh Networks”, IEEE Radio Communications, Vol.43, Issue-3, 2005, pp.23-30.
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[4] N.B. Salem, and J-P Hubaux, “Securing Wireless Mesh Networks”, IEEE Wireless Communications, Vol.13, Issue-2, 2006, pp.50-55.
[5] Shivlal Mewada, Umesh Kumar Singh and Pradeep Kumar Sharma, "A Novel Security Based Model for Wireless Mesh Networks", International Journal of Scientific Research in Network Security and Communication, Vol -01, Issue-01Mar -Apr 2013 , pp (11-15),
[6] C. Karlof and D. Wagner, “Secure routing in wireless sensor networks: attacks and countermeasures,” Ad Hoc Networks 1, 2003, pp. 293-315.
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Kamaran HamaAli.A.Faraj, "Web-based Teaching in Particular Developing Counties, Experience at “Sulamani University”," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.1-4, 2016.

Object detection and tracking is a very challenging task in image processing. This paper discusses about various object detection and tracking methods for crowded area such as airports, railway stations, shopping malls, residential apartment . Three major steps in video surveillance analysis are detection of moving objects, track those objects from frame to frame and recognise behaviours of those track objects to find out for suspicious activity detection. This paper mainly highlights the various object detection, object representation and object tracking techniques which are performed by different researchers in the past. This survey focused on different object detection using - background subtraction, fuzzy logic, temporal logic, hadoop, via temporal consistency modelling, nonflat static objects detection, abandoned detection of object in real time environments, detection of object on the basis of 3-dimensional image information. The usage of object tracking is for public safety, for efficient traffic management on roads , to fight against crime and terrorism.

Abandoned Object Detection, Surveillance Systems, Fuzzy Logic, Temporal Logic, Consistency Model Etc.

[1] Fahian Shahriar Mahin1, Md. Nazmul Islam1, Gerald Schaefer2, and Md. Atiqur Rahman Ahad1, "A Simple Approach for Abandoned Object Detection", Int`l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV`15 | 427
[2] A. Singh, S. Sawan, M. Hanmandlu, V.K. Madasu, B.C. Lovell, " An abandoned object detection system based on dual background segmentation",2009 Advanced Video and Signal Based Surveillance,78-0-7695-3718-4/09 $25.00 © 2009 IEEE DOI 10.1109/AVSS.2009.74
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[6] Hui Kong, Member, IEEE, Jean-Yves Audibert ,and Jean Ponce , Fellow, IEEE "Detecting Abandoned Objects with a Moving Camera" TIP-05187-2009, accepted 2009, accepted
[7] Pallavi S. Bangare, Nilesh J. Uke,"Implementation of Abandoned Object Detection in Real Time Environment", International Journal of Computer Applications (0975 – 8887) Volume 57– No.12, November 2012
[8] YingLi Tian, Senior Member, IEEE, Rogerio Schmidt Feris, Member, IEEE, Haowei Liu, Student Member, IEEE, Arun Hampapur, Senior Member, IEEE, and Ming-Ting Sun, Fellow, Ieee,"Robust Detection of Abandoned and Removed Objects in Complex Surveillance Videos", ieee Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 41, No. 5, September 2011
[9] Yiliang Zeng 1, Jinhui Lan 1,*, Bin Ran 2, Jing Gao 1 and Jinlin Zou, "A Novel Abandoned Object Detection System Based on Three-Dimensional Image Information", Sensors 2015, 15
[10] Tejas Naren TN1, Shankar SiddharthKA2 , Venkat Krishnan S3,Sanjeevi LR4, "Abandoned Object Detection for Automated video Surveillance using Hadoop" , International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering.An ISO 3297: 2007 Certified Organization Vol. 3, Special Issue 3, April 2014
[11] Medha Bhargava • Chia-Chih Chen • M. S. Ryoo •J. K. Aggarwal,"Detection of object abandonment using temporal logic", Received: 24 January 2008 / Revised: 22 September 2008 / Accepted: 18 November 2008 / Published online: 15 January 2009
[12] Kevin Lin, Shen-Chi Chen, Chu-Song Chen, Daw-Tung Lin, Senior Member, IEEE, and Yi-Ping Hung "Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual Surveillance", IEEE Transactions On Information Forensics And Security, Vol. 10, No. 7, July 2015 1359
[13] Thi Thi Zin, Member, IEEE, Pyke Tin, Hiromitsu Hama, Member, IEEE,and Takashi Toriu, Member, IEEE, "Unattended Object Intelligent Analyzer for Consumer Video Surveillance", IEEE Transactions on Consumer Electronics, Vol. 57, No. 2, May 2011

[14] Sivabalakrishnan.M, Dr.D.Manjula," Adaptive Background subtraction in Dynamic Environments Using Fuzzy Logic", Sivabalakrishnan.M. et al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 270-273
[15] Shuai Zhang, Member, IEEE, Chong Wang, Member, IEEE, Shing-Chow Chan, Member, IEEE, Xiguang Wei, and Check-Hei Ho," New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Over Camera Network", IEEE SENSORS JOURNAL, VOL. 15, NO. 5, MAY 2015 2679
[16] Himani S. Parekh1, Darshak G. Thakore 2, Udesang K. Jaliya 3," A Survey on Object Detection and Tracking Methods", Vol. 2, Issue 2, February 2014 Copyright to IJIRCCE.
[17] Sanjivani Shantaiya, Kesari Verma, Kamal Mehta," Study and Analysis of Methods of ObjectDetection in Video, (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013

Sonali A. Hargude and S. R. Idate, "Survey on AIAOD: Advanced Intelligent Abandoned Object Detection," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.5-9, 2016.

The increase of e-learning resources such as interactive learning environments, learning management systems (LMS), intelligent tutoring systems (ITS), and hypermedia systems as well as the establishment of school databases of student test scores has created large repositories of data. These data can be converted into knowledge for enhancing teaching and learning process. This paper proposes a new learning model ELATE (Enhancing Learning And Teaching) for strengthening Mathematics education in school level and proposes a frame work for using Educational Data Mining for knowledge management. This model utilizes Educational Data Mining (EDM) methods to provide results to the learners regarding their performance and skill level and to the teachers about their wards performance and their capabilities. The teachers can use the EDM results to motivate the slow learners and move the over practiced students to the next level. The ELATE frame work proposed in this paper has five levels processing to provide knowledge management services to stakeholders of educational institutions especially for the teachers and students.

LMS, ITS, ELATE, Educational Data Mining

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[13] S. Lakshmi Prabha, Dr. A. R. Mohamed Shanavas, “ Analysing Students Performance Using Educational Data Mining Methods”, International Journal of Applied Engineering Research, , Vol. 10, Issue-82, pg. 667-671, 2015.
[14] S. Lakshmi Prabha, Dr. A. R. Mohamed Shanavas, “A Study on Learning Factor Analysis – An Educational Data Mining Technique for Student Knowledge Modeling”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol.17, Issue-6, Ver. IV, pp.95-101,. Nov – Dec. 2015.
[15] S. Lakshmi Prabha, Dr. A. R. Mohamed Shanavas, “Assessing Students Performance Using Learning Curves”, International Journal of Engineering and Techniques, Vol. 1 Issue-6, pp. 6-16, Jan - Feb 2016.
[16] S. Lakshmi Prabha A. R. Mohamed Shanavas, “Implementation of E-Learning Package for Mensuration-A Branch of Mathematics”, IEEE, pp.219-221, 2014.
[17] Tawseef Ayoub Shaikh1, Amit Chhabra,” Effect of WEKA Filters on the Performance of the NavieBayes Data Mining Algorithm on Arrhythmia and Parkinson’s Datasets”, International Journal of Computer Sciences and Engineering(IJCSE), Vol. 2, Issue-5, pp.45-51, May 2014.

S. Lakshmi Prabha and A.R.Mohamed Shanavas, "Elate – A New Student Learning Model Utilizing EDM for Strengthening Math Education," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.10-14, 2016.

Image denoising is the predominant task in the field of image processing and computer vision. The occurrence of noise is due to the influence of various internal and external sources that creates de trop signals, resulting in image noise. In medical images the presence of noise may leads to false clinical diagnosis. To prevent the contingence of noise, various image denoising algorithms are employed expeditiously to uproot the noise. Discrete Wavelet Transform (DWT) is employed to extinguish the occurrence of noise in medical images. It decomposes the input image into detailed and approximate coefficients at three levels. The sampled data is transformed into array of wavelet coefficients. Filters are introduced to eliminate the noises which are coupled with the input image. Wiener Filter, Adaptive Bilateral Filters (ABF) and Boundary Discriminative Noise Detection (BDND) are used to denoise the speckle noise and salt and pepper noise present in the Ultra Sound image. From these results it is observed that ABF filter works well against the speckle noise with the following metrics Peak Signal to Noise Ratio (PSNR), SSIM (Structural Similarity Index Measure), CoC (Coefficient of Correlation), EPI (Edge Preserving Index) for Medical images corrupted with noise.

Wavelet transform, Wiener filter, BDND filter, ABF

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Latha Rani G.L, Shajun Nisha.S , M.Mohammed Sathik, "Image Denoising in Ultra Sound image using DWT with various Filters," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.15-24, 2016.

The chief function of wireless sensor networks is to forecast and collect the data from the main demesne, process the data and then transmit this data to the destination node. Now, for proper functioning, it requires some energy efficient mechanism so as to make paths between the source (sensor nodes) and the sink node. The path to be chosen should be in such a way so that the lifetime of the network is greatly increased. Wireless sensor network is emerging field because of its wide applications and least cost. It is a wireless network which subsist a group of small sensor nodes which communicate through radio interface. These sensor nodes are composed of sensing, computation, communication and power as four basic elements. But limited energy, communication capability, storage and bandwidth are the main resource constraints. In WSNs, Energy is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. These are battery powered sensor nodes. These small batteries have limited power and also may not easily rechargeable or removable. Long communication distance between sensors and a sink can greatly drain the energy of sensors and reduce the lifetime of a network. In WSNs, energy is a big factor to be considered. Various techniques are used to optimize energy level of sensor nodes of WSN. In this paper, basics of WSN are discussed in terms of architecture of WSN and wireless sensor node. This paper also presents the types of WSN along with its challenges. As clustering is one of the techniques that can improve the efficiency of a node, so clustering and its parameters are also included in this paper.

WSN, Nodes, Energy, Communication, Efficiency, Clustering, Sensor, Network

[1] Beenish Ayaz, Alastair Allen, Marian Wiercigroch, “Dynamically Reconfigurable Routing Protocol Design for Underwater Wireless Sensor Network”, Proceedings of the 8th International Conference on Sensing Technology, Liverpool, UK, Sep 2-4, 2014.
[2] Ankita, “A Survey on Wireless Sensor Network based Approaches”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol 4, Issue 4, April 2014.
[3] Stefanos A. Nikolidakis, Dionisis Kandris, Dimitrios D. Vergados and Christos Douligeris, “Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering”, algorithms ISSN 1999-4893, Open Access, Algorithms, Pg- 29-42, 2013.
[4] Umesh B.N, Dr G Vasanth and Dr Siddaraju, “Energy Efficient Routing of Wireless Sensor Networks Using Virtual Backbone and life time Maximization of Nodes”, International Journal of Wireless & Mobile Networks (IJWMN), Vol 5, No.1, Feb 2013.
[5] Saraswati Mishra and Prabhjot Kaur, “Comparison of energy efficient data transmission approaches for flat wireless sensor networks”, International Journal of Advanced Smart Sensor Network Systems (IJASSN), Vol 4, Nov 3, July 2014.
[6] Avijit Mathur, Thomas Newe, “Comparison and overview of Wireless sensor network systems for Medical Applications”, Proceedings of the 8th International Conference on Sensing Technology, Liverpool, UK, Sep 2-4, 2014.
[7] S.Ranjitha and D. Prabakar and S. Karthik, "A Study on Security issues in Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Vol-03, Issue-09, Page No (50-53), Sep -2015.
[8] Amr M. Kishk, Nagy W. Messiha, Nawal A. El-Fishawy, Abdelrahman A. Alkafs, Ahmed H. Madian, "Proposed Jamming Removal Technique for Wireless Sensor Network", International Journal of Scientific Research in Network Security and Communication, Vol -03, Issue-02, Page No (1-14), Mar -Apr 2015

Er. Satish Kumar, "A Study of Wireless Sensor Networks- A Review," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.23-27, 2016.

A typical software development process has several stages; each with its own significance and dependency on the other. Each stage is often complex and generates a wide variety of data. Using data mining techniques, we can uncover hidden patterns from this data, measure the impact of each stage on the other and gather useful information to improve the software development process. The insights gained from the extracted knowledge patterns can help software engineers to predict, plan and comprehend the various intricacies of the project, allowing them to optimize future software development activities. As every stage in the development process entails a certain outcome or goal, it becomes crucial to select the best data mining techniques to achieve these goals efficiently. In this paper, we survey the available data mining techniques and propose the most appropriate techniques for each stage of the development process. We also discuss how data mining improves the software development process in terms of time, cost, resources, reliability and maintainability.

Data Mining, Software Engineering, KDD methods, Software Development, Frequent Pattern Mining, Text Mining, Classification, Clustering

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Nidhin Thomas, Atharva Joshi, Rishikesh Misal and Manjula R, "Data Mining Techniques used in Software Engineering: A Survey," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.28-34, 2016.

Decision Trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have dealt with the issue of growing a decision tree from available data. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified or time-consuming. The primary comparison parameters are time and accuracy. Also shown efforts made for handling the change in the concept and they are compared in terms of memory, technique and accuracy. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.

Data steam, decision trees, information gain, Gaussian approximation

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S.Babu, G.Fathima , "Decision Trees for Mining Data Streams Based on the Gaussian Approximation," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.35-38, 2016.

Clustering problem is among the foremost quests in Machine Learning Paradigm. The Big Data sets, being versatile, multisourced & multivariate, could have noise, missing values, & may form clusters with arbitrary shape. Because of unpredictable nature of Big Data Sets, the clustering method should be able to handle missing values, noise, & should be able to make arbitrary shaped clusters. The partition based methods for clustering does not form non-convex clusters, The Hierarchical Clustering Methods & Algorithms are able to make arbitrary shaped clusters but they are not suitable for large data set due to time & computational complexity. Density & Grid Paradigm do not solve the issue related to missing values. Combining different Clustering Methods could eradicate the mutual issues they have pertaining to dataset’s geometrical and spatial properties, like missing data, non-convex shapes, noise etc.

Clustering, convex, non-convex, missing values, Big Data, noisy data, data mining, density based

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Sushant Bhargav and. Mahesh Pawar, "A Review of Clustering Methods forming Non-Convex clusters with, Missing and Noisy Data," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.39-44, 2016.

Clustering is the technique which used to group data from a set of unlabeled data, in a way that data containing similar properties contains in a same group. There are many cluster techniques are used to cluster data thus there is no suitable definition for cluster is available. Techniques like link based clustering, centroid based clustering, distribution based clustering, density based clustering are used. A survey over centroid based K-mean clustering techniques is presented which is widely used for clustering purpose. K-mean clustering technique suffers drawbacks like sensitive to initialization centroid, sensitive to noise, and there is no. of clusters also not defined. Thus an enhanced k-mean technique is presented to reduce such drawbacks and provide an enhanced functionality for clustering.

K-Means, Clustering, Centroid, Centroid based clustering, partition based clustering, point based, convex, euclidian

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Hitesh Chandra Mahawari and Mahesh Pawar , "A Study of Various Methods to find K for K-Means Clustering," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.45-47, 2016.

Cloud Comptuing is the act of utilizing a system of remote servers facilitated on the Internet to store, oversee, and prepare information, as opposed to a nearby server or an individual computer . The organization piece based procedures that are mindful from the server determination from the cloud can progress to the cost and adequacy of distributed computing. In this we concentrated on the distinctive swarm savvy based vitality proficient procedures called Ant settlement enhancement and Particle swarm streamlining based methods. There are different planning systems like the utilization of Ant settlement improvement has demonstrated a low convergence rate to the genuine worldwide least even at high quantities of measurements Artificial bee colony optimization algorithm has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Particle swarm advancement is restricted to introductory arrangement of particles, wrongly chose particles tends to poor results. In order to overcome these constrains a new hybrid Artificial bee colony and ant colony optimization algorithm for cloud computing environment will be proposed to enhance the energy consumption rate further.

Cloud Computing, Artificial bees colony , Ants colony optimization ,load balancing , scheduling

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Navdeep Kaur and Anil Kumar, "Hybrid Artificial Bee Colony and Ant Colony Optimization Based Power Aware Scheduling for Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.48-53, 2016.