Ali Ahmad Alawneh, Rashad Aouf

The undertaking of managing the Information systems projects has become a critical challenge for many information technology companies. This is due to the fact that these projects are complex with many diverse phases and activities; knowledge -intensive that depends heavily on the people's knowledge; and collaborative that involve many people working hand -by-hand for long period s of time. Therefore, there is a need for best utilizing the knowledge of all members of the project during all phases in order to get it done as needed. Actually it is the role of knowledge and its management as valuable resources for guaranteeing the su ccess of these projects and avoid repeating past mistakes. This paper attempts to present a new paradigm that combines the knowledge management processes (discovery, capture, sharing, and application) with the phases of information systems project manageme nt (initiation, planning, execution, and closing -down). It is expected that the application of the new paradigm by IT companies might improve the success rate of IS projects, through enhancing the activities of combination, exchange, socialization and transfer of knowledge and experience among project members. This might also lead to developing an organizational memory of a knowledge repository that shall serve as a reference for best practices and lessons learned that might in turn support the decision mak ing process for further projects.

Gali Suresh Reddy, T. V. Rajinikanth

Dimensionality reduction is very challenging and important in text mining. We need to know which features be retained what to be and It helps in reducing the processing overhead when performing text classification and text clustering. Another concern in text clustering and text classification is the similarity measure which we choose to find the similarity degree between any two text documents. In this paper, we work towa rds text clustering and text classification by addressing the use of the proposed similarity measure which is an improved version of our previous measure s. This proposed measure is used for supervised and un -supervised learning. The proposed measure overco mes the disadvantages of the existing measures.

Gunupudi Rajesh Kumar, NimmalaMangathayaru Researcher, Gugulothu Narsimha

This work discusses the ap proach for intrusion detection and classification by devising a membership function, inspired from Yung, Jung, & Shie-Jue (2014) and used in this work to carry the dimensionality reduction of processes present in the training set in evolutionary approach . The reduced process representation may then be used to perform classification and prediction for detecting intrusion. It is seen that the reduced representation of processes retains the system call distribution of the initial process. Experiment results show the proposed approach is better compared to existing approaches and helps in effective identification of U2R and R2L attacks.

Vangipuram Radhakrishna, P. V. Kumar, V. Janaki

A wide variety of real time applications generate temporal data. Determining and unearthing similar temporal association patterns is complex and challenging task when considering time stamped temporal databases. Previous works have considered only existin g distance measure and did not address retrieval and discovery of similar temporal association patterns using new distance measures. In this paper, we design a new similarity measure which suits the temporal context that can be applied to obtain all valid STAP (similar temporal association patterns). Our approach considers approximating the association pattern support bounds and applying the proposed similarity measure. The results show the proposed approach has better computational complexity compared to o ther approaches and improve d time efficiency.

Porika Sammulal, Yelipe UshaRani, Anurag Yepuri

Disease prediction and classification using medical record datasets is a challenging data mining research problem that also requires attribute value imputation to be carried implicitly. Medical datasets that are available in public databases are not free from missing values and this is also true when data is collected and sampled through various clinical trials. In this context, there is always a need to turn up with new approaches and methods for accurate and efficient analysis of medical records. Several imputation strategies are proposed in the literature and each of them have reported accuracies achieved on benchmark datasets. However, a better imputation approach always helps in improving classification accuracies and this in turn helps to more accurate disease prediction . An approach for imputing medical records is proposed in this paper. We name the approach as Class -Based-Clustering-Imputation (CBC-IM). Experiments are carried out on several benchmark datasets. Results achieved using our imputation approach is compared to existing imputation approaches using classifiers such as KNN, SVM and C4.5. The results show improved performance on most of the datasets and are almost nearer to remaining approaches discussed in this paper.

Arun Nagaraja, N. Rajasekhar

Privacy is the major concern in the present world today. Data is also playing the major role and how the data is secured in the network is the trivial task. When the data is transmitted in the network, it is initially encrypted and the information is secured with minimal crypto security features. The information is more secured with public and private keys and during retrieval the data is to be decrypted with the same. After gathering and transmitting the required information, how to provide privacy to the data in the network is concern. Data that is shared in the network are to be made private and more secured. By privacy we can preserve the data in any channel and transmit it with security and its standards. The paper discusses on how the recent trend is working on data and its security. The paper also tells about the various cryptographic algorithm used during data transmission.

T. V. Rajinikanth, G. Suresh Reddy

Feature dimensionality has always been one of the key challenges in text mining as it increases complexity when mining documents with high dimensionality. High dimensionality introduces sparseness, noise, and boosts the computational and space complexities. Dimensionality reduction is usually addressed by implementing either feature reduction or feature selection te chniques. In this work, the problem of dimensionality reduction is addressed using singular value decomposition and the results are compared to information gain approach through retaining top-k features. High dimensional clustering is carried by using k -means algorithm with gaussian function. The proposed dimensionality reduction and clustering approaches are compared to conventional approaches and results prove the importance of our approach.

Hassan Oudani, Salah-Ddine Krit, Lahoucine El Maimouni, Jalal Laassiri

Sensor networks are dense wireless networks of small, low -cost sensors, which collect and disseminate environmental data, it used in a variety of fields like military surveillance, habitat monitoring, monitoring and gathering events in hazardous environmen ts, surveillance of buildings, whether monitoring etc. In wireless sensor networks Flat and Hierarchical routing are two most typical routing protocols. Comparing the two routing protocols (flat / hierarchical) is very important to know well the performance of each routing, for that, in this paper we will discuss in first some of the major Flat routing protocols (AODV, DSDV, GSR, FSR, OLSR, SPIN) and hierarchical routing protocols (LEACH -C, LEACH-F, PEGASIS, ZHLS) for wireless sensor networks, and later we will compare and simulate the behavior on lifetime and energy using NS2 simulator for flat and hierarchical routing protocols.

Vangipuram Radhakrishna, P. V. Kumar, V. Janaki, Aravind Cheruvu

This research address the design of a new dissimilarity measure and applying it to find all valid similarity profiled patterns in a temporal database defined over finite number of time slots. The proposed dissimilarity measure is a function of the reference sequence, threshold and standard deviation. Given, a reference time sequence and allowable dissimilarity limit, unearthing all eccentric (similar) temporal association patterns requires a similarity or correlation measure that can estimate similar association patterns accurately, efficiently, and is computationally optimal. This research also proposes a method to estimate temporal pattern support bounds. The experiment result shows the advantage of our proposed measure and bound estimation approach and also proves that our method is computationally efficient when compared to naïve, sequential and Spamine approaches.

Hasan Rashaideh

A new adaptive Differential Evolution (DE) algorithm for finding a pproximate to the solutions of second-order Dirichlet problems is presented. The proposed adaptive algorithm reflected a variation of the finite difference scheme in the perspective that each of the derivatives are approximated by forward, backward, and central differences’ quotients. The major advantage of the novel adaptive algorithm over other numerical methods; it has no limitations on the nature of the problem, type of classification, and the number of mesh points. A test cases that include different classes and types of Dirichlet problems to demonstrate the efficiency and simplicity of the algorithm are presented. The numerical results obtained show strong agreement with exact solutions, and demonstrate reliability and great accuracy of the method.

Pedro Isaías

Editorial preface for Volume 12, Issue 1 of the IADIS International Journal on Computer Science and Information Systems (IJCSIS). This issue brings together peer-reviewed original research contributions covering the wide spectrum of Information Systems, Artificial Intelligence, Software Engineering, and Digital Transformation.