Muhammad Fahad

Muhammad Umer Qayyum, Muhammad Fahad, Nasrullah Abbasi

In oncology, recurrence after treatment is one of the major challenges, related to patients' survival and quality of life. Conventionally, prediction of cancer relapse has always relied on clinical observation with statistical model support, which almost fails to explain the complex, multifactorial nature of tumor recurrence. This research explores how AI and ML models may increase the accuracy and reliability of recurrence prediction in cancer. Therefore, AI and ML create new opportunities not only for personalized medicine but also for proactive management of patients through analyzing large volumes of data on genetics, clinical manifestations, and treatment. The paper describes the various AI and ML techniques for pattern identification and outcome prediction in cancer patients using supervised and unsupervised learning. Clinical implications provide an opportunity to review how early interventions could happen and the design of treatment planning.

Sheikh Muhamad Hizam, Waqas Ahmed, Muhammad Fahad et al.

A smart home is grounded on the sensors that endure automation, safety, and structural integration. The security mechanism in digital setup possesses vibrant prominence and the biometric facial recognition system is novel addition to accrue the smart home features. Understanding the implementation of such technology is the outcome of user behavior modeling. However, there is the paucity of empirical research that explains the role of cognitive, functional, and social aspects of end-users acceptance behavior towards biometric facial recognition systems at homes. Therefore, a causal research survey was conducted to comprehend the behavioral intention towards the use of a biometric facial recognition system. Technology Acceptance Model (TAM)was implied with Perceived System Quality (PSQ) and Social Influence (SI)to hypothesize the conceptual framework. Data was collected from 475respondents through online questionnaires. Structural Equation Modeling(SEM) and Artificial Neural Network (ANN) were employed to analyze the surveyed data. The results showed that all the variables of the proposed framework significantly affected the behavioral intention to use the system. The PSQ appeared as the noteworthy predictor towards biometric facial recognition system usability through regression and sensitivity analyses. A multi-analytical approach towards understanding the technology user behavior will support the efficient decision-making process in Human-centric computing.

Saira Khan, Khalid Iqbal, Safi Faizullah et al.

Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results into limitation of data reconstruction while securing the sensitive data. This paper presents a clustering based privacy preservation probabilistic model of big data to secure sensitive information..model to attain minimum perturbation and maximum privacy. In our model, sensitive information is secured after identifying the sensitive data from data clusters to modify or generalize it.The resulting dataset is analysed to calculate the accuracy level of our model in terms of hidden data, lossed data as result of reconstruction. Extensive experiements are carried out in order to demonstrate the results of our proposed model. Clustering based Privacy preservation of individual data in big data with minimum perturbation and successful reconstruction highlights the significance of our model in addition to the use of standard performance evaluation measures.

Nicolas Bus, Ana Roxin, Guillaume Picinbono et al.

Manually checking models for compliance against building regulation is a time-consuming task for architects and construction engineers. There is thus a need for algorithms that process information from construction projects and report non-compliant elements. Still automated code-compliance checking raises several obstacles. Building regulations are usually published as human readable texts and their content is often ambiguous or incomplete. Also, the vocabulary used for expressing such regulations is very different from the vocabularies used to express Building Information Models (BIM). Furthermore, the high level of details associated to BIM-contained geometries induces complex calculations. Finally, the level of complexity of the IFC standard also hinders the automation of IFC processing tasks. Model chart, formal rules and pre-processors approach allows translating construction regulations into semantic queries. We further demonstrate the usefulness of this approach through several use cases. We argue our approach is a step forward in bridging the gap between regulation texts and automated checking algorithms. Finally with the recent building ontology BOT recommended by the W3C Linked Building Data Community Group, we identify perspectives for standardizing and extending our approach.

Muhammad Fahad, Yi Guo, Brian Bingham et al.

Pollution plume monitoring using autonomous vehicles is important due to the adverse effect of pollution plumes on the environment and associated monetary losses. Using the advection-diffusion plume dispersion model, we present a control law design to track dynamic concentration level curves. We also present a gradient and divergence estimation method to enable this control law from concentration measurement only. We then present the field testing results of the control law to track concentration level curves in a plume generated using Rhodamine dye as a pollution surrogate in a near-shore marine environment. These plumes are then autonomously tracked using an unmanned surface vessel equipped with fluorometer sensors. Field experimental results are shown to evaluate the performance of the controller, and complexities of field experiments in real-world marine environments are discussed in the paper.

Hicham Elasri, Elmustapha Elabbassi, Sekkaki Abderrahim et al.

Recently, attention has focused on the software development, specially by differ-ent teams that are geographically distant to support collaborative work. Manage-ment, description and modeling in such collaborative approach are through sever-al tools and techniques based on UML models. It is now supported by a large number of tools. Most of these systems have the ability to compare different UML models, assist developers, designers and also provide operations for the merging and integration, to produce a coherent model. The contribution in this ar-ticle is both to integrate a set of UML class diagrams using mappings that are re-sult of alignment and assist designers and developers in the integration. In addi-tion, we will present a detail integration of UML models with the validation of mappings between them. Such validation helps to achieve correct, consistent and coherent integrated model.

Muhammad Fahad

Change management for evolving collaborative business process development is crucial when the business logic, transections and workflow change due to changes in business strategies or organizational and technical environment. During the change implementation, business processes are analyzed and improved ensuring that they capture the proposed change and they do not contain any undesired functionalities or change side-effects. This paper presents Business Process Change Management approach for the efficient and effective implementation of change in the business process. The key technology behind our approach is our proposed Business Process Change Management Ontology (BPCMont) which is the main contribution of this paper. BPCMont, as a formalized change specification, helps to revert BP into a consistent state in case of system crash, intermediate conflicting stage or unauthorized change done, aid in change traceability in the new and old versions of business processes, change effects can be seen and estimated effectively, ease for Stakeholders to validate and verify change implementation, etc.

Muhammad Fahad

In this paper, we present the results obtained by our DKP-AOM system within the OAEI 2015 campaign. DKP-AOM is an ontology merging tool designed to merge heterogeneous ontologies. In OAEI, we have participated with its ontology mapping component which serves as a basic module capable of matching large scale ontologies before their merging. This is our first successful participation in the Conference, OA4QA and Anatomy track of OAEI. DKP-AOM is participating with two versions (DKP-AOM and DKP-AOM_lite), DKP-AOM performs coherence analysis. In OA4QA track, DKPAOM out-performed in the evaluation and generated accurate alignments allowed to answer all the queries of the evaluation. We can also see its competitive results for the conference track in the evaluation initiative among other reputed systems. In the anatomy track, it has produced alignments within an allocated time and appeared in the list of systems which produce coherent results. Finally, we discuss some future work towards the development of DKP-AOM.