Requirement Validation for Embedded Systems in Automotive Industry Through Modeling

Requirement validation contributes significantly toward the success of software projects. Validating requirements is also essential to ensure the correctness of embedded systems in the auto industry. The auto industry emphasizes a lot on the verification of car designs and shapes. Invalid or erroneous requirements lead to inappropriate designs and degraded product quality. Considering the required expertise and time for requirement validation, significant attention is not devoted to verification and validation of requirements in the industry. Currently, the failure ratio of software projects is significantly higher and the key reason for that appears to be the inappropriate and invalidated requirements at the early stages in the projects. To that end, we propose a model-based approach that uses the existing V&V model. Through virtual prototyping, the proposed approach eliminates the need to validate the requirements after each stage of the project. Consequently, the model is validated after the design phase and the errors in requirements are detected at the earliest stage. In this research, we performed two different case studies for requirement validation in the auto industry by using a modeling-based approach and formal technique using Petri nets. A benefit of the proposed modeling-based approach is that the projects in the auto industry domain can be completed in less time due to effective requirements validation. Moreover, the modeling-based approach minimizes the development time, cost and increases productivity because the majority of the code is automatically generated using the approach

HateClassify: A Service Framework for Hate Speech Identication on Social Media

It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multi-class problem. In this work, we present the hate identification on the social media as a multi-label problem. To this end, we propose a CNN-based service framework called "HateClassify" for labeling the social media contents as the hate speech, offensive, or non-offensive. Results demonstrate that the multi-class classification accuracy for the CNN based approaches particularly Sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multi-label classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multi-label classification instead of multi-class classification, hate speech detection is increased up to 20%

On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction

Cancer is the second leading cause of mortality across the globe. Approximately 9.6 million people are estimated to have died due to cancer disease in 2019. Accurate and early prediction of cancer can assist healthcare professionals to devise timely therapeutic innervations to control sufferings and the risk of mortality. Generally, a machine learning (ML) based predictive system in healthcare uses data (genetic profile or clinical parameters) and learning algorithms to predict target values for cancer detection. However, optimization of predictive accuracy is an important endeavor for accurate decision making. Reject Option (RO) classifiers have been used to improve the predictive accuracy of classifiers for cancer like complex problems. In a gene profile all of the features are not important and should be shaved off. ML offers different techniques with their own methodology for feature selection (FS) and the classification results are dependent on the datasets each having its own distribution and features. Therefore, both FS methods and ML algorithms with RO need to be considered for robust classification. The main objective of this study is to optimize three parameters (learning algorithm, FS method and rejection rate) for robust cancer prediction rather than considering two traditional parameters (learning algorithm and rejection rate). The analysis of different FS methods (including t-Test, Las Vegas Filter (LVF), Relief, and Information Gain (IG)) and RO classifiers on different rejection thresholds is performed to investigate the robust predictability of cancer. The three cancer datasets (Colon cancer, Leukemia and Breast cancer) were reduced using different FS methods and each of them were used to analyze the predictability of cancer using different RO classifiers. The results reveal that for each dataset predictive accuracies of RO classifiers were different for different FS methods. The findings based on proposed scheme indicate that, the ML algorithms along with their dependence on suitable FS methods need to be taken into consideration for accurate prediction

Fixed point results for set-contractions on metric spaces with a directed graph

In this paper, we establish the existence of fixed points for set-valued mappings satisfying certain graph contractions with set-valued domain endowed with a graph. These results unify, generalize, and complement various known comparable results in the literature.King Fahd University of Petroleum and Minerals project IN 121023.http://link.springer.com/journal/11784hb201

Cloud Based Recommendation Services for Healthcare

With the inception of portable computing devices, enormous growth in the healthcare data over the Internet has been observed. Consequently, the Web based systems come across several challenges, such as storage, availability, reliability, and scalability. By employing the cloud computing to offer healthcare services helps in overcoming the aforementioned challenges. Besides the healthcare organizations, cloud computing services are also equally beneficial for general public in devising patient-centric or user-centric methodologies that involve users in managing health related activities. This dissertation proposes methodologies to: (a) make risk assessment about diseases and to identify health experts through social media using cloud based services, (b) recommend personalized health insurance plans, and (c) secure the personal health data in the cloud. The proposed disease risk assessment approach compares the profiles of enquiring users with the existing disease specific patient profiles and calculates the risk assessment score for that disease. The health expert consultation service permits users to consult with the health specialists that use Twitter by analyzing the tweets. The methodology employs Hyperlink-Induced Topic Search (HITS) based approach to distinguish between the doctors and non-doctors on the basis of tweets. For personalized health insurance plans identification, a recommendation framework to evaluate different health insurance plans from the cost and coverage perspectives is proposed. Multi-attribute Utility Theory (MAUT) is used to permit users evaluate health insurance plans using several criteria, for example premium, copay, deductibles, maximum out-of-pocket limit, and various other attributes. Moreover, a standardized representation of health insurance plans to overcome the heterogeneity issues is also presented. Furthermore, the dissertation presents a methodology to implement patient-centric access control over the patients’ health information shared in the cloud environment. This methodology ensures data confidentiality through the El-Gamal encryption and proxy re-encryption approaches. Moreover, the scheme permits the owners of health data to selectively grant access to users over the portions of health records based on the access level specified in the Access Control List (ACL) for different groups of users. Experimental results demonstrate the efficacy of the methodologies presented in the dissertation to offer patient/user-centric services and to overcome the scalability issues

Identifying design issues related to the knowledge bases of medical decision support systems

The modern medical diagnostic systems are based on the techniques using digital data formats – a natural feed for the computer based systems. With the use of modern diagnostic techniques the diagnosis process is becoming more complex as many diseases seem to have the same pre-symptoms at early stages. And of course computer based systems require more efficient and effective ways to identify such complexities. However, the existing formalisms for knowledge representation, tools and technologies, learning and reasoning strategies seem inadequate to create meaningful relationship among the entities of medical data i.e. diseases, symptoms and medicine etc. This inadequacy actually is due to the poor design of the knowledge base of the medical system and leads the medical systems towards inaccurate diagnosis. This thesis discusses the limitations and issues specific to the design factors of the knowledge base and suggests that instead of using the deficient approaches and tools for representing, learning and retrieving the accurate knowledge, use of semantic web tools and techniques should be adopted. Design by contract approach may be suitable for establishing the relationships between the diseases and symptoms. The relationship between diseases and symptoms and their invariants can be represented more meaningfully using semantic web. This can lead to more concrete diagnosis, by overcoming the deficiencies and limitations of traditional approaches and tools

e-Health Cloud: Privacy Concerns and Mitigation Strategies

Cloud based solutions have permeated in the healthcare domain due to a broad range of benefits offered by the cloud computing. Besides the financial advantages to the healthcare organizations, cloud computing also offers large-scale and on-demand storage and processing services to various entities of the cloud based health ecosystem. However, outsourcing the sensitive health information to the third-party cloud providers can result in serious privacy concerns. This chapter highlights the privacy issues related to the health-data and also presents privacy preserving requirements. Besides the benefits of the cloud computing in healthcare, cloud computing deployment models are also discussed from the perspective of healthcare systems. Moreover, some recently developed strategies to mitigate the privacy concerns and to fulfill the privacy preserving requirements are also discussed in detail. Furthermore, strengths and weaknesses of each of the presented strategies are reported and some open issues for the future research are also presented