A mathematical model and a solving strategy for the Pickup and Delivery Problem with three-dimensional loading constraints regarding a combinatorial configuration instead of a traditional approach that utilizes Boolean variables is proposed. A traditional one-to-one Pickup and Delivery Problem in a combination with a problem of packing transported items into vehicles by means of the proposed combinatorial generation algorithm is solved.

Fever has different causes and types, but with similar symptoms. Therefore, making fever diagnosis with human physiological symptoms more complicated. This research project delves into the design of a web based expert multi-fever diagnosis system using a novel fuzzy symptom classifier with human self-observed physiological symptoms. Considering malaria, Lassa, dengue, typhoid and yellow fever. The fuzzy-symptom classifier has two stages. Fist stage is fever type confirmation  using common fever symptoms, leading to five major fuzzy rules and the second phase is determining the level of infection (severe or mild) of the confirmed type of fever using unique fever symptoms. Furthermore, Case studies during the system implementation yielded data collected from 50 patients of having different types of fever. The analysis clearly shows the effectiveness and accuracy in the system performance through false result elimination. In addition, acceptability of the system was investigated through structured questionnaire administered to same 50 patients. This result clearly indicates that the system is well accepted, by users and considered fairly easy to use, time and cost saving.

The cloud storage services are used to store intermediate and persistent data generated from various resources including servers and IoT based networks. The outcome of such developments is that the data gets duplicated and gets replicated rapidly especially when large number of cloud users are working in a collaborative environment to solve large scale problems in geo-distributed networks. The data gets prone to breach of privacy and high incidence of duplication. When the dynamics of cloud services change over period of time, the ownership and proof of identity operations also need to change and work dynamically for high degree of security. In this work we will study the following concepts, methods and the schemes that can make the cloud services secure and reduce the incidence of data duplication. With the help of cryptography mathematics and to increase potential storage capacity. The proposed scheme works for deduplication of data with arithmetic key validity operations that reduce the overhead and increase the complexity of the keys so that it is hard to break the keys.

In order to respond effectively to rapid and unpredictable changes in the business environment, information systems are forced to adapt by developing agility skills, so the company can grow or even survive in this changing environment. Thus, it is necessary to provide organizations an approach indicating how to design, measure and improve the agility of their information systems. In this context an iterative, integrated, flexible and balanced framework has been developed. This framework, called ITAAM (Information Technology Agility Assessment Model), is founded upon a simple model, derived from the Balanced ScoreCard methodology that is widely used as a system of performance measurement. Basically, the structure of our approach is composed of a conceptual framework, an evaluation methodology, an agility grid and a scoring system for the Global Agility Index calculation. Moreover, within every assessment cycle, the agility level is identified and a set of recommendations are provided as well as the necessary adjustment guidelines concerning the whole organization. As a result, sustainability and continuous improvement of agility can be ensured, which represents the main goal of our new construct.

Software product line approach has been successfully adopted in various software domains. In some fields, single SPLs are no longer sufficient to fulfill their requirements due to the large variability amount they include. Consequently, a set of separated SPLs is built to handle this issue and construct what is known by Multi Product Lines (MPL). However, the emergence of MPLs results in several challenges, namely: managing the reuse between SPLs, structuring the MPL model and distributed derivation. In this paper, we propose a new approach for SPLs composition. Our approach relies on two main concepts: the separation of concerns and the partial derivation. It is validated in the context of an e-Learning MPL and an illustration is explained throughout the paper. The results show that our approach helps systemizing reuse within MPLs and structuring the MPL model. Moreover, SPLs are integrated early in the development process avoiding thus the distributed derivation challenges.

The contemporary era of technological quest is buzzing with two words - Big Data and Cloud Computing. Digital data is growing rapidly from Gigabytes (GBs), terabytes (TBs) to Petabytes (PBs), and thereby burgeoning data management challenges. Social networking sites like Twitter, Facebook, Google+ etc generate huge data chunks on daily basis. Among them, twitter masks as the largest source of publicly available mammoth data chunks intended for various objectives of research and development. In order to further research in this fast emerging area of managing Big Data, we propose a novel framework for doing analysis on Big Data and show its implementation by  creating a ‘Twitter Mart’ which is a compilation of subject specific tweets that address some of the challenges for industries engaged in analyzing subject specific data. In this paper, we adduce algorithms and an holistic model that aids in effective stockpiling and retrieving data in an efficient manner.

Biometric verification is a mean by which a person can be uniquely authenticated by evaluating some distinguishing biological traits. Fingerprinting is the ancient and the most widely used biometric authentication system today which is succeeded by other identifiers such as hand geometry, earlobe geometry, retina and iris patterns, voice waves and signature. Out of these verification methods, earlobe geometry proved to be most efficient and reliable option to be used either along with existing security system or alone for one level of security. However in previous work, the pre-processing was done manually and algorithms have not necessarily handled problems caused by hair and earing. In this paper, we present a more systematic, coherent and methodical way for ear identification using GLCM algorithm which has overcome the limitations of other successful algorithms like ICP and PCA. GLCM elucidates the texture of an image through a matrix formed by considering the number of occurrences of two pixels which are horizontally adjacent to each other in row and column. Pre-processing techniques and algorithms will be discussed and a step-by-step procedure to implement the system will be stated.

Model-driven testing is a method to verify the requirement specification of the system through UML models. Cuckoo search (CS) algorithm is based on the brooding characteristics of cuckoo birds. The test case generation process is used to identify the test cases with resources with critical domain requirements. This proposed paper emphasizing on the generation and optimization of test cases or test data using cuckoo search technique through a case study, i.e., the withdrawal operation in a Bank ATM and it also describes the generation of test cases from UML behavioral diagram like activity diagram, possible test paths are also generated through activity diagram graph.