Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.
The main assets of universities are students. The performance of students plays a vital role in producing excellent graduate students who will be the future viable leader and manpower in charge of a country's financial and societal progress. The purpose of this research is to develop a "University Students Result Analysis and Prediction System" that can help the students to predict their results and to identify their lacking so that they can put concentration to overcome these lacking and get better outcomes in the upcoming semesters. The prediction system can help not only the current students but also the upcoming students to find out exactly what they should do so that students can avoid poor achievement that will help to increase their academic results and other skills. To train the system, we collected data from the university student's database and directly from students by survey using Google form containing information, such as gender, extracurricular activities, no of tuition, programming skills, class test mark, assignment mark, attendance, and previous semester Grade Point Average (GPA), where the main aim is to relate to student performances and Cumulative GPA (CGPA). We use Weka tools to train the system and to develop the decision tree. In decision tree, the acquired knowledge can be expressed in a readable form and produced classification rules that are easy to understand than other classification techniques. These rules used to develop a web-based system that can predict the grade points of students from their previous records. Moreover, the system notifies students' lack and gives suggestions to improve their results. Finally, we compared the performance of three (J48, REPTree, and Hoeffding Tree) different decision tree algorithms, and comparative analysis shows that for our system, the J48 algorithm achieves the highest accuracy.
The Internet of Things (IoT) has become one of the most known terms in present times, reaching new levels and setting a trend in the world. Evidently, it is the future of connectivity which has turned physical objects into intelligent objects. Therefore, there has been a growing curiosity in the IoT field and this leads to the concept of IoT ecosystem. But the contemporary fragmented ecosystem of regulations, technologies, and systems slows IoT deployments. Thus, we consider multi-tiered computational infrastructure which would be feasible to provide services from the nearest possible location of end devices. To mitigate multi-tier infrastructure issues, Software-Defined Networking (SDN) steps in. The journey behind SDN-supported multi-tier computational infrastructure can be understood from this paper's elaborate study. Next, a comparative analysis on dew, roof, fog and cloud is conducted and the impact of internet on these computing paradigms is briefly explained. Then a novel framework termed "SD-DRFC (Software-Defined Dew, Roof, Fog and Cloud computing)" is proposed for today's IoT ecosystem. The role and functionality of each tier of SD-DRFC framework are adequately explained. A use case focused on the SD-DRFC framework is then presented and simulated by utilizing iFogSim simulator. To evaluate the efficiency of the presented SD-DRFC framework, four QoS parameters (Latency, Network Usage, Cost, and Energy Consumption) are considered in this paper. When comparing the simulation results, the presented SD-DRFC framework performs much better than cloud-only implementation. The advantages and suitability of utilizing this proposed framework have been demonstrated by multiple use-cases which range from conceptual visions to existing running systems.
In the internet of things (IoT) domain, there has currently been a growing interest, leading to the idea of the IoT ecosystem. But the standards, technology, and structures of the conventional IoT framework do not provide the necessary QoS for today's massive data. Thus, for today's IoT ecosystem, a framework called SD-DRFC (software-defined dew, roof, fog, and cloud computing) is suggested in this article. The framework delivers facilities from the closest possible position of end-user gadgets and thus increases the QoS in an IoT system. Clear description about the role and features of each tier is also presented. The path to a multi-tier computational architecture assisted by SDN can be realized from the given detailed literature review. Using the iFogSim simulator, a use case based on the architecture provided is then given and evaluated. This article considers four QoS parameters (latency, network use, cost, and energy consumption). When compared the findings of the simulation, the proposed framework execution performs much better than cloud-only execution.
The internet of things (IoT) offers a range of benefits for its users, ranging from quicker and more precise perception of our ecosystem to more cost-effective monitoring of manufacturing applications, by taking internet access to the things. Due to the ubiquitous existence of the internet, there's been an increasing pace in the IoT. Such a growing pace has brought about the term of IoT ecosystem. This exponential growing IoT ecosystem will encounter several challenges in its path. Computing domains were used from very initial stage to assist the IoT ecosystem and mitigate those challenges. To understand the impact of computing domains in IoT ecosystem, this paper performs the elaborative study on cloud, fog, roof, and dew computing including their interaction, benefits, and limitations in IoT ecosystem. The brief comparative analysis on these four computing domains are then performed. The impact of internet and offline computing on these computing domains are then analyzed in depth. Finally, this paper presents the suggestions of potential appropriate computing domain strategies for IoT ecosystems.
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